Instruction: What field is the article from?

Prompt:
Title: Learning to Discover Skills through Guidance
Abstract: In the field of unsupervised skill discovery (USD), a major challenge is
limited exploration, primarily due to substantial penalties when skills deviate
from their initial trajectories. To enhance exploration, recent methodologies
employ auxiliary rewards to maximize the epistemic uncertainty or entropy of
states. However, we have identified that the effectiveness of these rewards
declines as the environmental complexity rises. Therefore, we present a novel
USD algorithm, skill discovery with guidance (DISCO-DANCE), which (1) selects
the guide skill that possesses the highest potential to reach unexplored
states, (2) guides other skills to follow guide skill, then (3) the guided
skills are dispersed to maximize their discriminability in unexplored states.
Empirical evaluation demonstrates that DISCO-DANCE outperforms other USD
baselines in challenging environments, including two navigation benchmarks and
a continuous control benchmark. Qualitative visualizations and code of
DISCO-DANCE are available at https://mynsng.github.io/discodance.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation
Abstract: Nowadays, the rapid development of mobile economy has promoted the
flourishing of online marketing campaigns, whose success greatly hinges on the
efficient matching between user preferences and desired marketing campaigns
where a well-established Marketing-oriented Knowledge Graph (dubbed as MoKG)
could serve as the critical "bridge" for preference propagation. In this paper,
we seek to carefully prompt a Large Language Model (LLM) with domain-level
knowledge as a better marketing-oriented knowledge miner for marketing-oriented
knowledge graph construction, which is however non-trivial, suffering from
several inevitable issues in real-world marketing scenarios, i.e.,
uncontrollable relation generation of LLMs,insufficient prompting ability of a
single prompt, the unaffordable deployment cost of LLMs. To this end, we
propose PAIR, a novel Progressive prompting Augmented mIning fRamework for
harvesting marketing-oriented knowledge graph with LLMs. In particular, we
reduce the pure relation generation to an LLM based adaptive relation filtering
process through the knowledge-empowered prompting technique. Next, we steer
LLMs for entity expansion with progressive prompting augmentation,followed by a
reliable aggregation with comprehensive consideration of both self-consistency
and semantic relatedness. In terms of online serving, we specialize in a small
and white-box PAIR (i.e.,LightPAIR),which is fine-tuned with a high-quality
corpus provided by a strong teacher-LLM. Extensive experiments and practical
applications in audience targeting verify the effectiveness of the proposed
(Light)PAIR.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification
Abstract: Large Language Models (LLMs) have demonstrated remarkable proficiency in
generating fluent text. However, they often encounter the challenge of
generating inaccurate or hallucinated content. This issue is common in both
non-retrieval-based generation and retrieval-augmented generation approaches,
and existing post-hoc rectification methods may not address the accumulated
hallucination errors that may be caused by the "snowballing" issue, especially
in reasoning tasks. To tackle these challenges, we introduce a novel approach
called Real-time Verification and Rectification (Ever). Instead of waiting
until the end of the generation process to rectify hallucinations, Ever employs
a real-time, step-wise generation and hallucination rectification strategy. The
primary objective is to detect and rectify hallucinations as they occur during
the text generation process. When compared to both retrieval-based and
non-retrieval-based baselines, Ever demonstrates a significant improvement in
generating trustworthy and factually accurate text across a diverse range of
tasks, including short-form QA, biography generation, and multi-hop reasoning.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: GSGFormer: Generative Social Graph Transformer for Multimodal Pedestrian Trajectory Prediction
Abstract: Pedestrian trajectory prediction, vital for selfdriving cars and
socially-aware robots, is complicated due to intricate interactions between
pedestrians, their environment, and other Vulnerable Road Users. This paper
presents GSGFormer, an innovative generative model adept at predicting
pedestrian trajectories by considering these complex interactions and offering
a plethora of potential modal behaviors. We incorporate a heterogeneous graph
neural network to capture interactions between pedestrians, semantic maps, and
potential destinations. The Transformer module extracts temporal features,
while our novel CVAE-Residual-GMM module promotes diverse behavioral modality
generation. Through evaluations on multiple public datasets, GSGFormer not only
outperforms leading methods with ample data but also remains competitive when
data is limited.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Multi-View Causal Representation Learning with Partial Observability
Abstract: We present a unified framework for studying the identifiability of
representations learned from simultaneously observed views, such as different
data modalities. We allow a partially observed setting in which each view
constitutes a nonlinear mixture of a subset of underlying latent variables,
which can be causally related. We prove that the information shared across all
subsets of any number of views can be learned up to a smooth bijection using
contrastive learning and a single encoder per view. We also provide graphical
criteria indicating which latent variables can be identified through a simple
set of rules, which we refer to as identifiability algebra. Our general
framework and theoretical results unify and extend several previous works on
multi-view nonlinear ICA, disentanglement, and causal representation learning.
We experimentally validate our claims on numerical, image, and multi-modal data
sets. Further, we demonstrate that the performance of prior methods is
recovered in different special cases of our setup. Overall, we find that access
to multiple partial views enables us to identify a more fine-grained
representation, under the generally milder assumption of partial observability.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospects
Abstract: The complexity of psychological principles underscore a significant societal
challenge, given the vast social implications of psychological problems.
Bridging the gap between understanding these principles and their actual
clinical and real-world applications demands rigorous exploration and adept
implementation. In recent times, the swift advancement of highly adaptive and
reusable artificial intelligence (AI) models has emerged as a promising way to
unlock unprecedented capabilities in the realm of psychology. This paper
emphasizes the importance of performance validation for these large-scale AI
models, emphasizing the need to offer a comprehensive assessment of their
verification from diverse perspectives. Moreover, we review the cutting-edge
advancements and practical implementations of these expansive models in
psychology, highlighting pivotal work spanning areas such as social media
analytics, clinical nursing insights, vigilant community monitoring, and the
nuanced exploration of psychological theories. Based on our review, we project
an acceleration in the progress of psychological fields, driven by these
large-scale AI models. These future generalist AI models harbor the potential
to substantially curtail labor costs and alleviate social stress. However, this
forward momentum will not be without its set of challenges, especially when
considering the paradigm changes and upgrades required for medical
instrumentation and related applications.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: MDFL: Multi-domain Diffusion-driven Feature Learning
Abstract: High-dimensional images, known for their rich semantic information, are
widely applied in remote sensing and other fields. The spatial information in
these images reflects the object's texture features, while the spectral
information reveals the potential spectral representations across different
bands. Currently, the understanding of high-dimensional images remains limited
to a single-domain perspective with performance degradation. Motivated by the
masking texture effect observed in the human visual system, we present a
multi-domain diffusion-driven feature learning network (MDFL) , a scheme to
redefine the effective information domain that the model really focuses on.
This method employs diffusion-based posterior sampling to explicitly consider
joint information interactions between the high-dimensional manifold structures
in the spectral, spatial, and frequency domains, thereby eliminating the
influence of masking texture effects in visual models. Additionally, we
introduce a feature reuse mechanism to gather deep and raw features of
high-dimensional data. We demonstrate that MDFL significantly improves the
feature extraction performance of high-dimensional data, thereby providing a
powerful aid for revealing the intrinsic patterns and structures of such data.
The experimental results on three multi-modal remote sensing datasets show that
MDFL reaches an average overall accuracy of 98.25%, outperforming various
state-of-the-art baseline schemes. The code will be released, contributing to
the computer vision community.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Herd: Using multiple, smaller LLMs to match the performances of proprietary, large LLMs via an intelligent composer
Abstract: Currently, over a thousand LLMs exist that are multi-purpose and are capable
of performing real world tasks, including Q&A, text summarization, content
generation, etc. However, accessibility, scale and reliability of free models
prevents them from being widely deployed in everyday use cases. To address the
first two issues of access and scale, organisations such as HuggingFace have
created model repositories where users have uploaded model weights and
quantized versions of models trained using different paradigms, as well as
model cards describing their training process. While some models report
performance on commonly used benchmarks, not all do, and interpreting the real
world impact of trading off performance on a benchmark for model deployment
cost, is unclear. Here, we show that a herd of open source models can match or
exceed the performance of proprietary models via an intelligent router. We show
that a Herd of open source models is able to match the accuracy of ChatGPT,
despite being composed of models that are effectively 2.5x smaller. We show
that in cases where GPT is not able to answer the query, Herd is able to
identify a model that can, at least 40% of the time.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Graph Convolutions Enrich the Self-Attention in Transformers!
Abstract: Transformers, renowned for their self-attention mechanism, have achieved
state-of-the-art performance across various tasks in natural language
processing, computer vision, time-series modeling, etc. However, one of the
challenges with deep Transformer models is the oversmoothing problem, where
representations across layers converge to indistinguishable values, leading to
significant performance degradation. We interpret the original self-attention
as a simple graph filter and redesign it from a graph signal processing (GSP)
perspective. We propose graph-filter-based self-attention (GFSA) to learn a
general yet effective one, whose complexity, however, is slightly larger than
that of the original self-attention mechanism. We demonstrate that GFSA
improves the performance of Transformers in various fields, including computer
vision, natural language processing, graph pattern classification, speech
recognition, and code classification.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms
Abstract: Continual Learning (CL) allows applications such as user personalization and
household robots to learn on the fly and adapt to context. This is an important
feature when context, actions, and users change. However, enabling CL on
resource-constrained embedded systems is challenging due to the limited labeled
data, memory, and computing capacity. In this paper, we propose LifeLearner, a
hardware-aware meta continual learning system that drastically optimizes system
resources (lower memory, latency, energy consumption) while ensuring high
accuracy. Specifically, we (1) exploit meta-learning and rehearsal strategies
to explicitly cope with data scarcity issues and ensure high accuracy, (2)
effectively combine lossless and lossy compression to significantly reduce the
resource requirements of CL and rehearsal samples, and (3) developed
hardware-aware system on embedded and IoT platforms considering the hardware
characteristics. As a result, LifeLearner achieves near-optimal CL performance,
falling short by only 2.8% on accuracy compared to an Oracle baseline. With
respect to the state-of-the-art (SOTA) Meta CL method, LifeLearner drastically
reduces the memory footprint (by 178.7x), end-to-end latency by 80.8-94.2%, and
energy consumption by 80.9-94.2%. In addition, we successfully deployed
LifeLearner on two edge devices and a microcontroller unit, thereby enabling
efficient CL on resource-constrained platforms where it would be impractical to
run SOTA methods and the far-reaching deployment of adaptable CL in a
ubiquitous manner. Code is available at
https://github.com/theyoungkwon/LifeLearner.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Confidence Is All You Need for MI Attacks
Abstract: In this evolving era of machine learning security, membership inference
attacks have emerged as a potent threat to the confidentiality of sensitive
data. In this attack, adversaries aim to determine whether a particular point
was used during the training of a target model. This paper proposes a new
method to gauge a data point's membership in a model's training set. Instead of
correlating loss with membership, as is traditionally done, we have leveraged
the fact that training examples generally exhibit higher confidence values when
classified into their actual class. During training, the model is essentially
being 'fit' to the training data and might face particular difficulties in
generalization to unseen data. This asymmetry leads to the model achieving
higher confidence on the training data as it exploits the specific patterns and
noise present in the training data. Our proposed approach leverages the
confidence values generated by the machine learning model. These confidence
values provide a probabilistic measure of the model's certainty in its
predictions and can further be used to infer the membership of a given data
point. Additionally, we also introduce another variant of our method that
allows us to carry out this attack without knowing the ground truth(true class)
of a given data point, thus offering an edge over existing label-dependent
attack methods.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations
Abstract: Self-supervised representation learning often uses data augmentations to
induce some invariance to "style" attributes of the data. However, with
downstream tasks generally unknown at training time, it is difficult to deduce
a priori which attributes of the data are indeed "style" and can be safely
discarded. To address this, we introduce a more principled approach that seeks
to disentangle style features rather than discard them. The key idea is to add
multiple style embedding spaces where: (i) each is invariant to all-but-one
augmentation; and (ii) joint entropy is maximized. We formalize our structured
data-augmentation procedure from a causal latent-variable-model perspective,
and prove identifiability of both content and (multiple blocks of) style
variables. We empirically demonstrate the benefits of our approach on synthetic
datasets and then present promising but limited results on ImageNet.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Non-approximability of constructive global $\mathcal{L}^2$ minimizers by gradient descent in Deep Learning
Abstract: We analyze geometric aspects of the gradient descent algorithm in Deep
Learning (DL) networks. In particular, we prove that the globally minimizing
weights and biases for the $\mathcal{L}^2$ cost obtained constructively in
[Chen-Munoz Ewald 2023] for underparametrized ReLU DL networks can generically
not be approximated via the gradient descent flow. We therefore conclude that
the method introduced in [Chen-Munoz Ewald 2023] is disjoint from the gradient
descent method.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Graphical Object-Centric Actor-Critic
Abstract: There have recently been significant advances in the problem of unsupervised
object-centric representation learning and its application to downstream tasks.
The latest works support the argument that employing disentangled object
representations in image-based object-centric reinforcement learning tasks
facilitates policy learning. We propose a novel object-centric reinforcement
learning algorithm combining actor-critic and model-based approaches to utilize
these representations effectively. In our approach, we use a transformer
encoder to extract object representations and graph neural networks to
approximate the dynamics of an environment. The proposed method fills a
research gap in developing efficient object-centric world models for
reinforcement learning settings that can be used for environments with discrete
or continuous action spaces. Our algorithm performs better in a visually
complex 3D robotic environment and a 2D environment with compositional
structure than the state-of-the-art model-free actor-critic algorithm built
upon transformer architecture and the state-of-the-art monolithic model-based
algorithm.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning
Abstract: Academic tabular benchmarks often contain small sets of curated features. In
contrast, data scientists typically collect as many features as possible into
their datasets, and even engineer new features from existing ones. To prevent
overfitting in subsequent downstream modeling, practitioners commonly use
automated feature selection methods that identify a reduced subset of
informative features. Existing benchmarks for tabular feature selection
consider classical downstream models, toy synthetic datasets, or do not
evaluate feature selectors on the basis of downstream performance. Motivated by
the increasing popularity of tabular deep learning, we construct a challenging
feature selection benchmark evaluated on downstream neural networks including
transformers, using real datasets and multiple methods for generating
extraneous features. We also propose an input-gradient-based analogue of Lasso
for neural networks that outperforms classical feature selection methods on
challenging problems such as selecting from corrupted or second-order features.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models
Abstract: Diffusion models have achieved significant success in image and video
generation. This motivates a growing interest in video editing tasks, where
videos are edited according to provided text descriptions. However, most
existing approaches only focus on video editing for short clips and rely on
time-consuming tuning or inference. We are the first to propose Video
Instruction Diffusion (VIDiff), a unified foundation model designed for a wide
range of video tasks. These tasks encompass both understanding tasks (such as
language-guided video object segmentation) and generative tasks (video editing
and enhancement). Our model can edit and translate the desired results within
seconds based on user instructions. Moreover, we design an iterative
auto-regressive method to ensure consistency in editing and enhancing long
videos. We provide convincing generative results for diverse input videos and
written instructions, both qualitatively and quantitatively. More examples can
be found at our website https://ChenHsing.github.io/VIDiff.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Exploring Adversarial Robustness of LiDAR-Camera Fusion Model in Autonomous Driving
Abstract: Our study assesses the adversarial robustness of LiDAR-camera fusion models
in 3D object detection. We introduce an attack technique that, by simply adding
a limited number of physically constrained adversarial points above a car, can
make the car undetectable by the fusion model. Experimental results reveal that
even without changes to the image data channel, the fusion model can be
deceived solely by manipulating the LiDAR data channel. This finding raises
safety concerns in the field of autonomous driving. Further, we explore how the
quantity of adversarial points, the distance between the front-near car and the
LiDAR-equipped car, and various angular factors affect the attack success rate.
We believe our research can contribute to the understanding of multi-sensor
robustness, offering insights and guidance to enhance the safety of autonomous
driving.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention
Abstract: We present Self-Adaptive Robust Attention for Robotics Transformers
(SARA-RT): a new paradigm for addressing the emerging challenge of scaling up
Robotics Transformers (RT) for on-robot deployment. SARA-RT relies on the new
method of fine-tuning proposed by us, called up-training. It converts
pre-trained or already fine-tuned Transformer-based robotic policies of
quadratic time complexity (including massive billion-parameter
vision-language-action models or VLAs), into their efficient linear-attention
counterparts maintaining high quality. We demonstrate the effectiveness of
SARA-RT by speeding up: (a) the class of recently introduced RT-2 models, the
first VLA robotic policies pre-trained on internet-scale data, as well as (b)
Point Cloud Transformer (PCT) robotic policies operating on large point clouds.
We complement our results with the rigorous mathematical analysis providing
deeper insight into the phenomenon of SARA.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphs
Abstract: Knowledge graphs (KGs) comprise entities interconnected by relations of
different semantic meanings. KGs are being used in a wide range of
applications. However, they inherently suffer from incompleteness, i.e.
entities or facts about entities are missing. Consequently, a larger body of
works focuses on the completion of missing information in KGs, which is
commonly referred to as link prediction (LP). This task has traditionally and
extensively been studied in the transductive setting, where all entities and
relations in the testing set are observed during training. Recently, several
works have tackled the LP task under more challenging settings, where entities
and relations in the test set may be unobserved during training, or appear in
only a few facts. These works are known as inductive, few-shot, and zero-shot
link prediction. In this work, we conduct a systematic review of existing works
in this area. A thorough analysis leads us to point out the undesirable
existence of diverging terminologies and task definitions for the
aforementioned settings, which further limits the possibility of comparison
between recent works. We consequently aim at dissecting each setting
thoroughly, attempting to reveal its intrinsic characteristics. A unifying
nomenclature is ultimately proposed to refer to each of them in a simple and
consistent manner.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Language-Conditioned Semantic Search-Based Policy for Robotic Manipulation Tasks
Abstract: Reinforcement learning and Imitation Learning approaches utilize policy
learning strategies that are difficult to generalize well with just a few
examples of a task. In this work, we propose a language-conditioned semantic
search-based method to produce an online search-based policy from the available
demonstration dataset of state-action trajectories. Here we directly acquire
actions from the most similar manipulation trajectories found in the dataset.
Our approach surpasses the performance of the baselines on the CALVIN benchmark
and exhibits strong zero-shot adaptation capabilities. This holds great
potential for expanding the use of our online search-based policy approach to
tasks typically addressed by Imitation Learning or Reinforcement Learning-based
policies.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Large Language Models' Understanding of Math: Source Criticism and Extrapolation
Abstract: It has been suggested that large language models such as GPT-4 have acquired
some form of understanding beyond the correlations among the words in text
including some understanding of mathematics as well. Here, we perform a
critical inquiry into this claim by evaluating the mathematical understanding
of the GPT-4 model. Considering that GPT-4's training set is a secret, it is
not straightforward to evaluate whether the model's correct answers are based
on a mathematical understanding or based on replication of proofs that the
model has seen before. We specifically craft mathematical questions which their
formal proofs are not readily available on the web, proofs that are more likely
not seen by the GPT-4. We see that GPT-4 is unable to solve those problems
despite their simplicity. It is hard to find scientific evidence suggesting
that GPT-4 has acquired an understanding of even basic mathematical concepts. A
straightforward way to find failure modes of GPT-4 in theorem proving is to
craft questions where their formal proofs are not available on the web. Our
finding suggests that GPT-4's ability is to reproduce, rephrase, and polish the
mathematical proofs that it has seen before, and not in grasping mathematical
concepts. We also see that GPT-4's ability to prove mathematical theorems is
continuously expanding over time despite the claim that it is a fixed model. We
suggest that the task of proving mathematical theorems in formal language is
comparable to the methods used in search engines such as Google while
predicting the next word in a sentence may be a misguided approach, a recipe
that often leads to excessive extrapolation and eventual failures. Prompting
the GPT-4 over and over may benefit the GPT-4 and the OpenAI, but we question
whether it is valuable for machine learning or for theorem proving.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models
Abstract: Achieving human-like planning and control with multimodal observations in an
open world is a key milestone for more functional generalist agents. Existing
approaches can handle certain long-horizon tasks in an open world. However,
they still struggle when the number of open-world tasks could potentially be
infinite and lack the capability to progressively enhance task completion as
game time progresses. We introduce JARVIS-1, an open-world agent that can
perceive multimodal input (visual observations and human instructions),
generate sophisticated plans, and perform embodied control, all within the
popular yet challenging open-world Minecraft universe. Specifically, we develop
JARVIS-1 on top of pre-trained multimodal language models, which map visual
observations and textual instructions to plans. The plans will be ultimately
dispatched to the goal-conditioned controllers. We outfit JARVIS-1 with a
multimodal memory, which facilitates planning using both pre-trained knowledge
and its actual game survival experiences. JARVIS-1 is the existing most general
agent in Minecraft, capable of completing over 200 different tasks using
control and observation space similar to humans. These tasks range from
short-horizon tasks, e.g., "chopping trees" to long-horizon tasks, e.g.,
"obtaining a diamond pickaxe". JARVIS-1 performs exceptionally well in
short-horizon tasks, achieving nearly perfect performance. In the classic
long-term task of $\texttt{ObtainDiamondPickaxe}$, JARVIS-1 surpasses the
reliability of current state-of-the-art agents by 5 times and can successfully
complete longer-horizon and more challenging tasks. The project page is
available at https://craftjarvis.org/JARVIS-1

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: GO-DICE: Goal-Conditioned Option-Aware Offline Imitation Learning via Stationary Distribution Correction Estimation
Abstract: Offline imitation learning (IL) refers to learning expert behavior solely
from demonstrations, without any additional interaction with the environment.
Despite significant advances in offline IL, existing techniques find it
challenging to learn policies for long-horizon tasks and require significant
re-training when task specifications change. Towards addressing these
limitations, we present GO-DICE an offline IL technique for goal-conditioned
long-horizon sequential tasks. GO-DICE discerns a hierarchy of sub-tasks from
demonstrations and uses these to learn separate policies for sub-task
transitions and action execution, respectively; this hierarchical policy
learning facilitates long-horizon reasoning. Inspired by the expansive
DICE-family of techniques, policy learning at both the levels transpires within
the space of stationary distributions. Further, both policies are learnt with
goal conditioning to minimize need for retraining when task goals change.
Experimental results substantiate that GO-DICE outperforms recent baselines, as
evidenced by a marked improvement in the completion rate of increasingly
challenging pick-and-place Mujoco robotic tasks. GO-DICE is also capable of
leveraging imperfect demonstration and partial task segmentation when
available, both of which boost task performance relative to learning from
expert demonstrations alone.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Learning to Learn in Interactive Constraint Acquisition
Abstract: Constraint Programming (CP) has been successfully used to model and solve
complex combinatorial problems. However, modeling is often not trivial and
requires expertise, which is a bottleneck to wider adoption. In Constraint
Acquisition (CA), the goal is to assist the user by automatically learning the
model. In (inter)active CA, this is done by interactively posting queries to
the user, e.g., asking whether a partial solution satisfies their (unspecified)
constraints or not. While interac tive CA methods learn the constraints, the
learning is related to symbolic concept learning, as the goal is to learn an
exact representation. However, a large number of queries is still required to
learn the model, which is a major limitation. In this paper, we aim to
alleviate this limitation by tightening the connection of CA and Machine
Learning (ML), by, for the first time in interactive CA, exploiting statistical
ML methods. We propose to use probabilistic classification models to guide
interactive CA to generate more promising queries. We discuss how to train
classifiers to predict whether a candidate expression from the bias is a
constraint of the problem or not, using both relation-based and scope-based
features. We then show how the predictions can be used in all layers of
interactive CA: the query generation, the scope finding, and the lowest-level
constraint finding. We experimentally evaluate our proposed methods using
different classifiers and show that our methods greatly outperform the state of
the art, decreasing the number of queries needed to converge by up to 72%.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero
Abstract: Artificial Intelligence (AI) systems have made remarkable progress, attaining
super-human performance across various domains. This presents us with an
opportunity to further human knowledge and improve human expert performance by
leveraging the hidden knowledge encoded within these highly performant AI
systems. Yet, this knowledge is often hard to extract, and may be hard to
understand or learn from. Here, we show that this is possible by proposing a
new method that allows us to extract new chess concepts in AlphaZero, an AI
system that mastered the game of chess via self-play without human supervision.
Our analysis indicates that AlphaZero may encode knowledge that extends beyond
the existing human knowledge, but knowledge that is ultimately not beyond human
grasp, and can be successfully learned from. In a human study, we show that
these concepts are learnable by top human experts, as four top chess
grandmasters show improvements in solving the presented concept prototype
positions. This marks an important first milestone in advancing the frontier of
human knowledge by leveraging AI; a development that could bear profound
implications and help us shape how we interact with AI systems across many AI
applications.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: GVFs in the Real World: Making Predictions Online for Water Treatment
Abstract: In this paper we investigate the use of reinforcement-learning based
prediction approaches for a real drinking-water treatment plant. Developing
such a prediction system is a critical step on the path to optimizing and
automating water treatment. Before that, there are many questions to answer
about the predictability of the data, suitable neural network architectures,
how to overcome partial observability and more. We first describe this dataset,
and highlight challenges with seasonality, nonstationarity, partial
observability, and heterogeneity across sensors and operation modes of the
plant. We then describe General Value Function (GVF) predictions -- discounted
cumulative sums of observations -- and highlight why they might be preferable
to classical n-step predictions common in time series prediction. We discuss
how to use offline data to appropriately pre-train our temporal difference
learning (TD) agents that learn these GVF predictions, including how to select
hyperparameters for online fine-tuning in deployment. We find that the
TD-prediction agent obtains an overall lower normalized mean-squared error than
the n-step prediction agent. Finally, we show the importance of learning in
deployment, by comparing a TD agent trained purely offline with no online
updating to a TD agent that learns online. This final result is one of the
first to motivate the importance of adapting predictions in real-time, for
non-stationary high-volume systems in the real world.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Minimal Macro-Based Rewritings of Formal Languages: Theory and Applications in Ontology Engineering (and beyond)
Abstract: In this paper, we introduce the problem of rewriting finite formal languages
using syntactic macros such that the rewriting is minimal in size. We present
polynomial-time algorithms to solve variants of this problem and show their
correctness. To demonstrate the practical relevance of the proposed problems
and the feasibility and effectiveness of our algorithms in practice, we apply
these to biomedical ontologies authored in OWL. We find that such rewritings
can significantly reduce the size of ontologies by capturing repeated
expressions with macros. In addition to offering valuable assistance in
enhancing ontology quality and comprehension, the presented approach introduces
a systematic way of analysing and evaluating features of rewriting systems
(including syntactic macros, templates, or other forms of rewriting rules) in
terms of their influence on computational problems.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: CMOSE: Comprehensive Multi-Modality Online Student Engagement Dataset with High-Quality Labels
Abstract: Online learning is a rapidly growing industry due to its convenience.
However, a major challenge in online learning is whether students are as
engaged as they are in face-to-face classes. An engagement recognition system
can significantly improve the learning experience in online classes. Current
challenges in engagement detection involve poor label quality in the dataset,
intra-class variation, and extreme data imbalance. To address these problems,
we present the CMOSE dataset, which contains a large number of data in
different engagement levels and high-quality labels generated according to the
psychological advice. We demonstrate the advantage of transferability by
analyzing the model performance on other engagement datasets. We also developed
a training mechanism, MocoRank, to handle the intra-class variation, the
ordinal relationship between different classes, and the data imbalance problem.
MocoRank outperforms prior engagement detection losses, achieving a 1.32%
enhancement in overall accuracy and 5.05% improvement in average accuracy. We
further demonstrate the effectiveness of multi-modality by conducting ablation
studies on features such as pre-trained video features, high-level facial
features, and audio features.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Combating the "Sameness" in AI Art: Reflections on the Interactive AI Installation Fencing Hallucination
Abstract: The article summarizes three types of "sameness" issues in Artificial
Intelligence(AI) art, each occurring at different stages of development in AI
image creation tools. Through the Fencing Hallucination project, the article
reflects on the design of AI art production in alleviating the sense of
uniformity, maintaining the uniqueness of images from an AI image synthesizer,
and enhancing the connection between the artworks and the audience. This paper
endeavors to stimulate the creation of distinctive AI art by recounting the
efforts and insights derived from the Fencing Hallucination project, all
dedicated to addressing the issue of "sameness".

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Instruction-Following Evaluation for Large Language Models
Abstract: One core capability of Large Language Models (LLMs) is to follow natural
language instructions. However, the evaluation of such abilities is not
standardized: Human evaluations are expensive, slow, and not objectively
reproducible, while LLM-based auto-evaluation is potentially biased or limited
by the ability of the evaluator LLM. To overcome these issues, we introduce
Instruction-Following Eval (IFEval) for large language models. IFEval is a
straightforward and easy-to-reproduce evaluation benchmark. It focuses on a set
of "verifiable instructions" such as "write in more than 400 words" and
"mention the keyword of AI at least 3 times". We identified 25 types of those
verifiable instructions and constructed around 500 prompts, with each prompt
containing one or more verifiable instructions. We show evaluation results of
two widely available LLMs on the market. Our code and data can be found at
https://github.com/google-research/google-research/tree/master/instruction_following_eval

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamics
Abstract: Simulating fluid dynamics is crucial for the design and development process,
ranging from simple valves to complex turbomachinery. Accurately solving the
underlying physical equations is computationally expensive. Therefore,
learning-based solvers that model interactions on meshes have gained interest
due to their promising speed-ups. However, it is unknown to what extent these
models truly understand the underlying physical principles and can generalize
rather than interpolate. Generalization is a key requirement for a
general-purpose fluid simulator, which should adapt to different topologies,
resolutions, or thermodynamic ranges. We propose SURF, a benchmark designed to
test the $\textit{generalization}$ of learned graph-based fluid simulators.
SURF comprises individual datasets and provides specific performance and
generalization metrics for evaluating and comparing different models. We
empirically demonstrate the applicability of SURF by thoroughly investigating
the two state-of-the-art graph-based models, yielding new insights into their
generalization.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations
Abstract: Existing research predominantly focuses on developing powerful language
learning models (LLMs) for mathematical reasoning within monolingual languages,
with few explorations in preserving efficacy in a multilingual context. To
bridge this gap, this paper pioneers exploring and training powerful
Multilingual Math Reasoning (xMR) LLMs. Firstly, by utilizing translation, we
construct the first multilingual math reasoning instruction dataset,
MGSM8KInstruct, encompassing ten distinct languages, thus addressing the issue
of training data scarcity in xMR tasks. Based on the collected dataset, we
propose different training strategies to build powerful xMR LLMs, named
MathOctopus, notably outperform conventional open-source LLMs and exhibit
superiority over ChatGPT in few-shot scenarios. Notably, MathOctopus-13B
reaches 47.6% accuracy which exceeds ChatGPT 46.3% on MGSM testset. Beyond
remarkable results, we unearth several pivotal observations and insights from
extensive experiments: (1) When extending the rejection sampling strategy to
the multilingual context, it proves effective for model performances, albeit
limited. (2) Employing parallel corpora for math Supervised Fine-Tuning (SFT)
across multiple languages not only significantly enhances model performance
multilingually but also elevates their monolingual performance. This indicates
that crafting multilingual corpora can be regarded as a vital strategy for
enhancing model performance in a specific language, especially in mathematical
reasoning tasks. For instance, MathOctopus-7B improves its counterparts that
trained on English from 42.2% to 50.8% on GSM8K testset.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: NERIF: GPT-4V for Automatic Scoring of Drawn Models
Abstract: Scoring student-drawn models is time-consuming. Recently released GPT-4V
provides a unique opportunity to advance scientific modeling practices by
leveraging the powerful image processing capability. To test this ability
specifically for automatic scoring, we developed a method NERIF
(Notation-Enhanced Rubric Instruction for Few-shot Learning) employing
instructional note and rubrics to prompt GPT-4V to score students' drawn models
for science phenomena. We randomly selected a set of balanced data (N = 900)
that includes student-drawn models for six modeling assessment tasks. Each
model received a score from GPT-4V ranging at three levels: 'Beginning,'
'Developing,' or 'Proficient' according to scoring rubrics. GPT-4V scores were
compared with human experts' scores to calculate scoring accuracy. Results show
that GPT-4V's average scoring accuracy was mean =.51, SD = .037. Specifically,
average scoring accuracy was .64 for the 'Beginning' class, .62 for the
'Developing' class, and .26 for the 'Proficient' class, indicating that more
proficient models are more challenging to score. Further qualitative study
reveals how GPT-4V retrieves information from image input, including problem
context, example evaluations provided by human coders, and students' drawing
models. We also uncovered how GPT-4V catches the characteristics of
student-drawn models and narrates them in natural language. At last, we
demonstrated how GPT-4V assigns scores to student-drawn models according to the
given scoring rubric and instructional notes. Our findings suggest that the
NERIF is an effective approach for employing GPT-4V to score drawn models. Even
though there is space for GPT-4V to improve scoring accuracy, some mis-assigned
scores seemed interpretable to experts. The results of this study show that
utilizing GPT-4V for automatic scoring of student-drawn models is promising.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Invariant Representation Learning via Decoupling Style and Spurious Features
Abstract: This paper considers the out-of-distribution (OOD) generalization problem
under the setting that both style distribution shift and spurious features
exist and domain labels are missing. This setting frequently arises in
real-world applications and is underlooked because previous approaches mainly
handle either of these two factors. The critical challenge is decoupling style
and spurious features in the absence of domain labels. To address this
challenge, we first propose a structural causal model (SCM) for the image
generation process, which captures both style distribution shift and spurious
features. The proposed SCM enables us to design a new framework called IRSS,
which can gradually separate style distribution and spurious features from
images by introducing adversarial neural networks and multi-environment
optimization, thus achieving OOD generalization. Moreover, it does not require
additional supervision (e.g., domain labels) other than the images and their
corresponding labels. Experiments on benchmark datasets demonstrate that IRSS
outperforms traditional OOD methods and solves the problem of Invariant risk
minimization (IRM) degradation, enabling the extraction of invariant features
under distribution shift.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data
Abstract: Advances in high-throughput sequencing technology have led to significant
progress in measuring gene expressions at the single-cell level. The amount of
publicly available single-cell RNA-seq (scRNA-seq) data is already surpassing
50M records for humans with each record measuring 20,000 genes. This highlights
the need for unsupervised representation learning to fully ingest these data,
yet classical transformer architectures are prohibitive to train on such data
in terms of both computation and memory. To address this challenge, we propose
a novel asymmetric encoder-decoder transformer for scRNA-seq data, called
xTrimoGene$^\alpha$ (or xTrimoGene for short), which leverages the sparse
characteristic of the data to scale up the pre-training. This scalable design
of xTrimoGene reduces FLOPs by one to two orders of magnitude compared to
classical transformers while maintaining high accuracy, enabling us to train
the largest transformer models over the largest scRNA-seq dataset today. Our
experiments also show that the performance of xTrimoGene improves as we scale
up the model sizes, and it also leads to SOTA performance over various
downstream tasks, such as cell type annotation, perturb-seq effect prediction,
and drug combination prediction. xTrimoGene model is now available for use as a
service via the following link: https://api.biomap.com/xTrimoGene/apply.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Synergistic Signals: Exploiting Co-Engagement and Semantic Links via Graph Neural Networks
Abstract: Given a set of candidate entities (e.g. movie titles), the ability to
identify similar entities is a core capability of many recommender systems.
Most often this is achieved by collaborative filtering approaches, i.e. if
users co-engage with a pair of entities frequently enough, the embeddings
should be similar. However, relying on co-engagement data alone can result in
lower-quality embeddings for new and unpopular entities. We study this problem
in the context recommender systems at Netflix. We observe that there is
abundant semantic information such as genre, content maturity level, themes,
etc. that complements co-engagement signals and provides interpretability in
similarity models. To learn entity similarities from both data sources
holistically, we propose a novel graph-based approach called SemanticGNN.
SemanticGNN models entities, semantic concepts, collaborative edges, and
semantic edges within a large-scale knowledge graph and conducts representation
learning over it. Our key technical contributions are twofold: (1) we develop a
novel relation-aware attention graph neural network (GNN) to handle the
imbalanced distribution of relation types in our graph; (2) to handle web-scale
graph data that has millions of nodes and billions of edges, we develop a novel
distributed graph training paradigm. The proposed model is successfully
deployed within Netflix and empirical experiments indicate it yields up to 35%
improvement in performance on similarity judgment tasks.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms
Abstract: We analyze asynchronous-type algorithms for distributed SGD in the
heterogeneous setting, where each worker has its own computation and
communication speeds, as well as data distribution. In these algorithms,
workers compute possibly stale and stochastic gradients associated with their
local data at some iteration back in history and then return those gradients to
the server without synchronizing with other workers. We present a unified
convergence theory for non-convex smooth functions in the heterogeneous regime.
The proposed analysis provides convergence for pure asynchronous SGD and its
various modifications. Moreover, our theory explains what affects the
convergence rate and what can be done to improve the performance of
asynchronous algorithms. In particular, we introduce a novel asynchronous
method based on worker shuffling. As a by-product of our analysis, we also
demonstrate convergence guarantees for gradient-type algorithms such as SGD
with random reshuffling and shuffle-once mini-batch SGD. The derived rates
match the best-known results for those algorithms, highlighting the tightness
of our approach. Finally, our numerical evaluations support theoretical
findings and show the good practical performance of our method.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Leveraging Hyperbolic Embeddings for Coarse-to-Fine Robot Design
Abstract: Multi-cellular robot design aims to create robots comprised of numerous cells
that can be efficiently controlled to perform diverse tasks. Previous research
has demonstrated the ability to generate robots for various tasks, but these
approaches often optimize robots directly in the vast design space, resulting
in robots with complicated morphologies that are hard to control. In response,
this paper presents a novel coarse-to-fine method for designing multi-cellular
robots. Initially, this strategy seeks optimal coarse-grained robots and
progressively refines them. To mitigate the challenge of determining the
precise refinement juncture during the coarse-to-fine transition, we introduce
the Hyperbolic Embeddings for Robot Design (HERD) framework. HERD unifies
robots of various granularity within a shared hyperbolic space and leverages a
refined Cross-Entropy Method for optimization. This framework enables our
method to autonomously identify areas of exploration in hyperbolic space and
concentrate on regions demonstrating promise. Finally, the extensive empirical
studies on various challenging tasks sourced from EvoGym show our approach's
superior efficiency and generalization capability.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Automated Planning Techniques for Elementary Proofs in Abstract Algebra
Abstract: This paper explores the application of automated planning to automated
theorem proving, which is a branch of automated reasoning concerned with the
development of algorithms and computer programs to construct mathematical
proofs. In particular, we investigate the use of planning to construct
elementary proofs in abstract algebra, which provides a rigorous and axiomatic
framework for studying algebraic structures such as groups, rings, fields, and
modules. We implement basic implications, equalities, and rules in both
deterministic and non-deterministic domains to model commutative rings and
deduce elementary results about them. The success of this initial
implementation suggests that the well-established techniques seen in automated
planning are applicable to the relatively newer field of automated theorem
proving. Likewise, automated theorem proving provides a new, challenging domain
for automated planning.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Open-Set Graph Anomaly Detection via Normal Structure Regularisation
Abstract: This paper considers an under-explored Graph Anomaly Detection (GAD) task,
namely open-set GAD, which aims to detect anomalous nodes using a small number
of labelled training normal and anomaly nodes (known as seen anomalies) that
cannot illustrate all possible inference-time abnormalities. The task has
attracted growing attention due to the availability of anomaly prior knowledge
from the label information that can help to substantially reduce detection
errors. However, current methods tend to over-emphasise fitting the seen
anomalies, leading to a weak generalisation ability to detect unseen anomalies,
i.e., those that are not illustrated by the labelled anomaly nodes. Further,
they were introduced to handle Euclidean data, failing to effectively capture
important non-Euclidean features for GAD. In this work, we propose a novel
open-set GAD approach, namely normal structure regularisation (NSReg), to
leverage the rich normal graph structure embedded in the labelled nodes to
tackle the aforementioned two issues. In particular, NSReg trains an
anomaly-discriminative supervised graph anomaly detector, with a plug-and-play
regularisation term to enforce compact, semantically-rich representations of
normal nodes. To this end, the regularisation is designed to differentiate
various types of normal nodes, including labelled normal nodes that are
connected in their local neighbourhood, and those that are not connected. By
doing so, it helps incorporate strong normality into the supervised anomaly
detector learning, mitigating their overfitting to the seen anomalies.
Extensive empirical results on real-world datasets demonstrate the superiority
of our proposed NSReg for open-set GAD.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Machine Learning and Knowledge: Why Robustness Matters
Abstract: Trusting machine learning algorithms requires having confidence in their
outputs. Confidence is typically interpreted in terms of model reliability,
where a model is reliable if it produces a high proportion of correct outputs.
However, model reliability does not address concerns about the robustness of
machine learning models, such as models relying on the wrong features or
variations in performance based on context. I argue that the epistemic
dimension of trust can instead be understood through the concept of knowledge,
where the trustworthiness of an algorithm depends on whether its users are in
the position to know that its outputs are correct. Knowledge requires beliefs
to be formed for the right reasons and to be robust to error, so machine
learning algorithms can only provide knowledge if they work well across
counterfactual scenarios and if they make decisions based on the right
features. This, I argue, can explain why we should care about model properties
like interpretability, causal shortcut independence, and distribution shift
robustness even if such properties are not required for model reliability.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Alchemist: Parametric Control of Material Properties with Diffusion Models
Abstract: We propose a method to control material attributes of objects like roughness,
metallic, albedo, and transparency in real images. Our method capitalizes on
the generative prior of text-to-image models known for photorealism, employing
a scalar value and instructions to alter low-level material properties.
Addressing the lack of datasets with controlled material attributes, we
generated an object-centric synthetic dataset with physically-based materials.
Fine-tuning a modified pre-trained text-to-image model on this synthetic
dataset enables us to edit material properties in real-world images while
preserving all other attributes. We show the potential application of our model
to material edited NeRFs.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness
Abstract: Counterfactual fairness requires that a person would have been classified in
the same way by an AI or other algorithmic system if they had a different
protected class, such as a different race or gender. This is an intuitive
standard, as reflected in the U.S. legal system, but its use is limited because
counterfactuals cannot be directly observed in real-world data. On the other
hand, group fairness metrics (e.g., demographic parity or equalized odds) are
less intuitive but more readily observed. In this paper, we use $\textit{causal
context}$ to bridge the gaps between counterfactual fairness, robust
prediction, and group fairness. First, we motivate counterfactual fairness by
showing that there is not necessarily a fundamental trade-off between fairness
and accuracy because, under plausible conditions, the counterfactually fair
predictor is in fact accuracy-optimal in an unbiased target distribution.
Second, we develop a correspondence between the causal graph of the
data-generating process and which, if any, group fairness metrics are
equivalent to counterfactual fairness. Third, we show that in three common
fairness contexts$\unicode{x2013}$measurement error, selection on label, and
selection on predictors$\unicode{x2013}$counterfactual fairness is equivalent
to demographic parity, equalized odds, and calibration, respectively.
Counterfactual fairness can sometimes be tested by measuring relatively simple
group fairness metrics.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Cognitive Architecture Toward Common Ground Sharing Among Humans and Generative AIs: Trial on Model-Model Interactions in Tangram Naming Task
Abstract: For generative AIs to be trustworthy, establishing transparent common
grounding with humans is essential. As a preparation toward human-model common
grounding, this study examines the process of model-model common grounding. In
this context, common ground is defined as a cognitive framework shared among
agents in communication, enabling the connection of symbols exchanged between
agents to the meanings inherent in each agent. This connection is facilitated
by a shared cognitive framework among the agents involved. In this research, we
focus on the tangram naming task (TNT) as a testbed to examine the
common-ground-building process. Unlike previous models designed for this task,
our approach employs generative AIs to visualize the internal processes of the
model. In this task, the sender constructs a metaphorical image of an abstract
figure within the model and generates a detailed description based on this
image. The receiver interprets the generated description from the partner by
constructing another image and reconstructing the original abstract figure.
Preliminary results from the study show an improvement in task performance
beyond the chance level, indicating the effect of the common cognitive
framework implemented in the models. Additionally, we observed that incremental
backpropagations leveraging successful communication cases for a component of
the model led to a statistically significant increase in performance. These
results provide valuable insights into the mechanisms of common grounding made
by generative AIs, improving human communication with the evolving intelligent
machines in our future society.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Control in Hybrid Chatbots
Abstract: Customer data typically is held in database systems, which can be seen as
rule-based knowledge base, whereas businesses increasingly want to benefit from
the capabilities of large, pre-trained language models.
  In this technical report, we describe a case study of how a commercial rule
engine and an integrated neural chatbot may be integrated, and what level of
control that particular integration mode leads to. We also discuss alternative
ways (including past ways realized in other systems) how researchers strive to
maintain control and avoid what has recently been called model "hallucination".

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Towards Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs
Abstract: Recent advancements in multimodal large language models (MLLMs) have achieved
significant multimodal generation capabilities, akin to GPT-4. These models
predominantly map visual information into language representation space,
leveraging the vast knowledge and powerful text generation abilities of LLMs to
produce multimodal instruction-following responses. We could term this method
as LLMs for Vision because of its employing LLMs for visual-language
understanding, yet observe that these MLLMs neglect the potential of harnessing
visual knowledge to enhance overall capabilities of LLMs, which could be
regraded as Vision Enhancing LLMs. In this paper, we propose an approach called
MKS2, aimed at enhancing LLMs through empowering Multimodal Knowledge Storage
and Sharing in LLMs. Specifically, we introduce the Modular Visual Memory, a
component integrated into the internal blocks of LLMs, designed to store
open-world visual information efficiently. Additionally, we present a soft
Mixtures-of-Multimodal Experts architecture in LLMs to invoke multimodal
knowledge collaboration during generation. Our comprehensive experiments
demonstrate that MKS2 substantially augments the reasoning capabilities of LLMs
in contexts necessitating physical or commonsense knowledge. It also delivers
competitive results on multimodal benchmarks.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Learning principle and mathematical realization of the learning mechanism in the brain
Abstract: While deep learning has achieved remarkable success, there is no clear
explanation about why it works so well. In order to discuss this question
quantitatively, we need a mathematical framework that explains what learning is
in the first place. After several considerations, we succeeded in constructing
a mathematical framework that can provide a unified understanding of all types
of learning, including deep learning and learning in the brain. We call it
learning principle, and it follows that all learning is equivalent to
estimating the probability of input data. We not only derived this principle,
but also mentioned its application to actual machine learning models. For
example, we found that conventional supervised learning is equivalent to
estimating conditional probabilities, and succeeded in making supervised
learning more effective and generalized. We also proposed a new method of
defining the values of estimated probability using differentiation, and showed
that unsupervised learning can be performed on arbitrary dataset without any
prior knowledge. Namely, this method is a general-purpose machine learning in
the true sense. Moreover, we succeeded in describing the learning mechanism in
the brain by considering the time evolution of a fully or partially connected
model and applying this new method. The learning principle provides solutions
to many unsolved problems in deep learning and cognitive neuroscience.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities
Abstract: The multimodal task of Visual Question Answering (VQA) encompassing elements
of Computer Vision (CV) and Natural Language Processing (NLP), aims to generate
answers to questions on any visual input. Over time, the scope of VQA has
expanded from datasets focusing on an extensive collection of natural images to
datasets featuring synthetic images, video, 3D environments, and various other
visual inputs. The emergence of large pre-trained networks has shifted the
early VQA approaches relying on feature extraction and fusion schemes to vision
language pre-training (VLP) techniques. However, there is a lack of
comprehensive surveys that encompass both traditional VQA architectures and
contemporary VLP-based methods. Furthermore, the VLP challenges in the lens of
VQA haven't been thoroughly explored, leaving room for potential open problems
to emerge. Our work presents a survey in the domain of VQA that delves into the
intricacies of VQA datasets and methods over the field's history, introduces a
detailed taxonomy to categorize the facets of VQA, and highlights the recent
trends, challenges, and scopes for improvement. We further generalize VQA to
multimodal question answering, explore tasks related to VQA, and present a set
of open problems for future investigation. The work aims to navigate both
beginners and experts by shedding light on the potential avenues of research
and expanding the boundaries of the field.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Fine-Grained Analysis of Team Collaborative Dialogue
Abstract: Natural language analysis of human collaborative chat dialogues is an
understudied domain with many unique challenges: a large number of dialogue act
labels, underspecified and dynamic tasks, interleaved topics, and long-range
contextual dependence. While prior work has studied broad metrics of team
dialogue and associated performance using methods such as LSA, there has been
little effort in generating fine-grained descriptions of team dynamics and
individual performance from dialogue. We describe initial work towards
developing an explainable analytics tool in the software development domain
using Slack chats mined from our organization, including generation of a novel,
hierarchical labeling scheme; design of descriptive metrics based on the
frequency of occurrence of dialogue acts; and initial results using a
transformer + CRF architecture to incorporate long-range context.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Harnessing Retrieval-Augmented Generation (RAG) for Uncovering Knowledge Gaps
Abstract: The paper presents a methodology for uncovering knowledge gaps on the
internet using the Retrieval Augmented Generation (RAG) model. By simulating
user search behaviour, the RAG system identifies and addresses gaps in
information retrieval systems. The study demonstrates the effectiveness of the
RAG system in generating relevant suggestions with a consistent accuracy of
93%. The methodology can be applied in various fields such as scientific
discovery, educational enhancement, research development, market analysis,
search engine optimisation, and content development. The results highlight the
value of identifying and understanding knowledge gaps to guide future
endeavours.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data
Abstract: Soft sensors are crucial in bridging autonomous systems' physical and digital
realms, enhancing sensor fusion and perception. Instead of deploying soft
sensors on the Cloud, this study shift towards employing on-device soft
sensors, promising heightened efficiency and bolstering data security. Our
approach substantially improves energy efficiency by deploying Artificial
Intelligence (AI) directly on devices within a wireless sensor network.
Furthermore, the synergistic integration of the Microcontroller Unit and
Field-Programmable Gate Array (FPGA) leverages the rapid AI inference
capabilities of the latter. Empirical evidence from our real-world use case
demonstrates that FPGA-based soft sensors achieve inference times ranging
remarkably from 1.04 to 12.04 microseconds. These compelling results highlight
the considerable potential of our innovative approach for executing real-time
inference tasks efficiently, thereby presenting a feasible alternative that
effectively addresses the latency challenges intrinsic to Cloud-based
deployments.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: xNeuSM: Explainable Neural Subgraph Matching with Graph Learnable Multi-hop Attention Networks
Abstract: Subgraph matching is a challenging problem with a wide range of applications
in database systems, biochemistry, and cognitive science. It involves
determining whether a given query graph is present within a larger target
graph. Traditional graph-matching algorithms provide precise results but face
challenges in large graph instances due to the NP-complete problem, limiting
their practical applicability. In contrast, recent neural network-based
approximations offer more scalable solutions, but often lack interpretable node
correspondences. To address these limitations, this article presents xNeuSM:
Explainable Neural Subgraph Matching which introduces Graph Learnable Multi-hop
Attention Networks (GLeMA) that adaptively learns the parameters governing the
attention factor decay for each node across hops rather than relying on fixed
hyperparameters. We provide a theoretical analysis establishing error bounds
for GLeMA's approximation of multi-hop attention as a function of the number of
hops. Additionally, we prove that learning distinct attention decay factors for
each node leads to a correct approximation of multi-hop attention. Empirical
evaluation on real-world datasets shows that xNeuSM achieves substantial
improvements in prediction accuracy of up to 34% compared to approximate
baselines and, notably, at least a seven-fold faster query time than exact
algorithms. The source code of our implementation is available at
https://github.com/martinakaduc/xNeuSM.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approach
Abstract: Federated learning (FL) enables collaboratively training a model while
keeping the training data decentralized and private. However, one significant
impediment to training a model using FL, especially large models, is the
resource constraints of devices with heterogeneous computation and
communication capacities as well as varying task sizes. Such heterogeneity
would render significant variations in the training time of clients, resulting
in a longer overall training time as well as a waste of resources in faster
clients. To tackle these heterogeneity issues, we propose the Dynamic
Tiering-based Federated Learning (DTFL) system where slower clients dynamically
offload part of the model to the server to alleviate resource constraints and
speed up training. By leveraging the concept of Split Learning, DTFL offloads
different portions of the global model to clients in different tiers and
enables each client to update the models in parallel via local-loss-based
training. This helps reduce the computation and communication demand on
resource-constrained devices and thus mitigates the straggler problem. DTFL
introduces a dynamic tier scheduler that uses tier profiling to estimate the
expected training time of each client, based on their historical training time,
communication speed, and dataset size. The dynamic tier scheduler assigns
clients to suitable tiers to minimize the overall training time in each round.
We first theoretically prove the convergence properties of DTFL. We then train
large models (ResNet-56 and ResNet-110) on popular image datasets (CIFAR-10,
CIFAR-100, CINIC-10, and HAM10000) under both IID and non-IID systems.
Extensive experimental results show that compared with state-of-the-art FL
methods, DTFL can significantly reduce the training time while maintaining
model accuracy.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Efficient Large Language Models: A Survey
Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in
important tasks such as natural language understanding, language generation,
and complex reasoning and have the potential to make a substantial impact on
our society. Such capabilities, however, come with the considerable resources
they demand, highlighting the strong need to develop effective techniques for
addressing their efficiency challenges. In this survey, we provide a systematic
and comprehensive review of efficient LLMs research. We organize the literature
in a taxonomy consisting of three main categories, covering distinct yet
interconnected efficient LLMs topics from model-centric, data-centric, and
framework-centric perspective, respectively. We have also created a GitHub
repository where we compile the papers featured in this survey at
https://github.com/AIoT-MLSys-Lab/EfficientLLMs,
https://github.com/AIoT-MLSys-Lab/Efficient-LLMs-Survey, and will actively
maintain this repository and incorporate new research as it emerges. We hope
our survey can serve as a valuable resource to help researchers and
practitioners gain a systematic understanding of the research developments in
efficient LLMs and inspire them to contribute to this important and exciting
field.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings
Abstract: Despite the great success of neural visual generative models in recent years,
integrating them with strong symbolic knowledge reasoning systems remains a
challenging task. The main challenges are two-fold: one is symbol assignment,
i.e. bonding latent factors of neural visual generators with meaningful symbols
from knowledge reasoning systems. Another is rule learning, i.e. learning new
rules, which govern the generative process of the data, to augment the
knowledge reasoning systems. To deal with these symbol grounding problems, we
propose a neural-symbolic learning approach, Abductive Visual Generation
(AbdGen), for integrating logic programming systems with neural visual
generative models based on the abductive learning framework. To achieve
reliable and efficient symbol assignment, the quantized abduction method is
introduced for generating abduction proposals by the nearest-neighbor lookups
within semantic codebooks. To achieve precise rule learning, the contrastive
meta-abduction method is proposed to eliminate wrong rules with positive cases
and avoid less-informative rules with negative cases simultaneously.
Experimental results on various benchmark datasets show that compared to the
baselines, AbdGen requires significantly fewer instance-level labeling
information for symbol assignment. Furthermore, our approach can effectively
learn underlying logical generative rules from data, which is out of the
capability of existing approaches.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Neural Reasoning About Agents' Goals, Preferences, and Actions
Abstract: We propose the Intuitive Reasoning Network (IRENE) - a novel neural model for
intuitive psychological reasoning about agents' goals, preferences, and actions
that can generalise previous experiences to new situations. IRENE combines a
graph neural network for learning agent and world state representations with a
transformer to encode the task context. When evaluated on the challenging Baby
Intuitions Benchmark, IRENE achieves new state-of-the-art performance on three
out of its five tasks - with up to 48.9% improvement. In contrast to existing
methods, IRENE is able to bind preferences to specific agents, to better
distinguish between rational and irrational agents, and to better understand
the role of blocking obstacles. We also investigate, for the first time, the
influence of the training tasks on test performance. Our analyses demonstrate
the effectiveness of IRENE in combining prior knowledge gained during training
for unseen evaluation tasks.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Diffusion Model Alignment Using Direct Preference Optimization
Abstract: Large language models (LLMs) are fine-tuned using human comparison data with
Reinforcement Learning from Human Feedback (RLHF) methods to make them better
aligned with users' preferences. In contrast to LLMs, human preference learning
has not been widely explored in text-to-image diffusion models; the best
existing approach is to fine-tune a pretrained model using carefully curated
high quality images and captions to improve visual appeal and text alignment.
We propose Diffusion-DPO, a method to align diffusion models to human
preferences by directly optimizing on human comparison data. Diffusion-DPO is
adapted from the recently developed Direct Preference Optimization (DPO), a
simpler alternative to RLHF which directly optimizes a policy that best
satisfies human preferences under a classification objective. We re-formulate
DPO to account for a diffusion model notion of likelihood, utilizing the
evidence lower bound to derive a differentiable objective. Using the Pick-a-Pic
dataset of 851K crowdsourced pairwise preferences, we fine-tune the base model
of the state-of-the-art Stable Diffusion XL (SDXL)-1.0 model with
Diffusion-DPO. Our fine-tuned base model significantly outperforms both base
SDXL-1.0 and the larger SDXL-1.0 model consisting of an additional refinement
model in human evaluation, improving visual appeal and prompt alignment. We
also develop a variant that uses AI feedback and has comparable performance to
training on human preferences, opening the door for scaling of diffusion model
alignment methods.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Imitation Bootstrapped Reinforcement Learning
Abstract: Despite the considerable potential of reinforcement learning (RL), robotics
control tasks predominantly rely on imitation learning (IL) owing to its better
sample efficiency. However, given the high cost of collecting extensive
demonstrations, RL is still appealing if it can utilize limited imitation data
for efficient autonomous self-improvement. Existing RL methods that utilize
demonstrations either initialize the replay buffer with demonstrations and
oversample them during RL training, which does not benefit from the
generalization potential of modern IL methods, or pretrain the RL policy with
IL on the demonstrations, which requires additional mechanisms to prevent
catastrophic forgetting during RL fine-tuning. We propose imitation
bootstrapped reinforcement learning (IBRL), a novel framework that first trains
an IL policy on a limited number of demonstrations and then uses it to propose
alternative actions for both online exploration and target value bootstrapping.
IBRL achieves SoTA performance and sample efficiency on 7 challenging sparse
reward continuous control tasks in simulation while learning directly from
pixels. As a highlight of our method, IBRL achieves $6.4\times$ higher success
rate than RLPD, a strong method that combines the idea of oversampling
demonstrations with modern RL improvements, under the budget of 10 demos and
100K interactions in the challenging PickPlaceCan task in the Robomimic
benchmark.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: QFree: A Universal Value Function Factorization for Multi-Agent Reinforcement Learning
Abstract: Centralized training is widely utilized in the field of multi-agent
reinforcement learning (MARL) to assure the stability of training process. Once
a joint policy is obtained, it is critical to design a value function
factorization method to extract optimal decentralized policies for the agents,
which needs to satisfy the individual-global-max (IGM) principle. While
imposing additional limitations on the IGM function class can help to meet the
requirement, it comes at the cost of restricting its application to more
complex multi-agent environments. In this paper, we propose QFree, a universal
value function factorization method for MARL. We start by developing
mathematical equivalent conditions of the IGM principle based on the advantage
function, which ensures that the principle holds without any compromise,
removing the conservatism of conventional methods. We then establish a more
expressive mixing network architecture that can fulfill the equivalent
factorization. In particular, the novel loss function is developed by
considering the equivalent conditions as regularization term during policy
evaluation in the MARL algorithm. Finally, the effectiveness of the proposed
method is verified in a nonmonotonic matrix game scenario. Moreover, we show
that QFree achieves the state-of-the-art performance in a general-purpose
complex MARL benchmark environment, Starcraft Multi-Agent Challenge (SMAC).

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Games
Abstract: In this work, we introduce a self-supervised behavior cloning transformer for
text games, which are challenging benchmarks for multi-step reasoning in
virtual environments. Traditionally, Behavior Cloning Transformers excel in
such tasks but rely on supervised training data. Our approach auto-generates
training data by exploring trajectories (defined by common macro-action
sequences) that lead to reward within the games, while determining the
generality and utility of these trajectories by rapidly training small models
then evaluating their performance on unseen development games. Through
empirical analysis, we show our method consistently uncovers generalizable
training data, achieving about 90\% performance of supervised systems across
three benchmark text games.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Assessing the Robustness of Intelligence-Driven Reinforcement Learning
Abstract: Robustness to noise is of utmost importance in reinforcement learning
systems, particularly in military contexts where high stakes and uncertain
environments prevail. Noise and uncertainty are inherent features of military
operations, arising from factors such as incomplete information, adversarial
actions, or unpredictable battlefield conditions. In RL, noise can critically
impact decision-making, mission success, and the safety of personnel. Reward
machines offer a powerful tool to express complex reward structures in RL
tasks, enabling the design of tailored reinforcement signals that align with
mission objectives. This paper considers the problem of the robustness of
intelligence-driven reinforcement learning based on reward machines. The
preliminary results presented suggest the need for further research in
evidential reasoning and learning to harden current state-of-the-art
reinforcement learning approaches before being mission-critical-ready.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Offline RL with Observation Histories: Analyzing and Improving Sample Complexity
Abstract: Offline reinforcement learning (RL) can in principle synthesize more optimal
behavior from a dataset consisting only of suboptimal trials. One way that this
can happen is by "stitching" together the best parts of otherwise suboptimal
trajectories that overlap on similar states, to create new behaviors where each
individual state is in-distribution, but the overall returns are higher.
However, in many interesting and complex applications, such as autonomous
navigation and dialogue systems, the state is partially observed. Even worse,
the state representation is unknown or not easy to define. In such cases,
policies and value functions are often conditioned on observation histories
instead of states. In these cases, it is not clear if the same kind of
"stitching" is feasible at the level of observation histories, since two
different trajectories would always have different histories, and thus "similar
states" that might lead to effective stitching cannot be leveraged.
Theoretically, we show that standard offline RL algorithms conditioned on
observation histories suffer from poor sample complexity, in accordance with
the above intuition. We then identify sufficient conditions under which offline
RL can still be efficient -- intuitively, it needs to learn a compact
representation of history comprising only features relevant for action
selection. We introduce a bisimulation loss that captures the extent to which
this happens, and propose that offline RL can explicitly optimize this loss to
aid worst-case sample complexity. Empirically, we show that across a variety of
tasks either our proposed loss improves performance, or the value of this loss
is already minimized as a consequence of standard offline RL, indicating that
it correlates well with good performance.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Pelvic floor MRI segmentation based on semi-supervised deep learning
Abstract: The semantic segmentation of pelvic organs via MRI has important clinical
significance. Recently, deep learning-enabled semantic segmentation has
facilitated the three-dimensional geometric reconstruction of pelvic floor
organs, providing clinicians with accurate and intuitive diagnostic results.
However, the task of labeling pelvic floor MRI segmentation, typically
performed by clinicians, is labor-intensive and costly, leading to a scarcity
of labels. Insufficient segmentation labels limit the precise segmentation and
reconstruction of pelvic floor organs. To address these issues, we propose a
semi-supervised framework for pelvic organ segmentation. The implementation of
this framework comprises two stages. In the first stage, it performs
self-supervised pre-training using image restoration tasks. Subsequently,
fine-tuning of the self-supervised model is performed, using labeled data to
train the segmentation model. In the second stage, the self-supervised
segmentation model is used to generate pseudo labels for unlabeled data.
Ultimately, both labeled and unlabeled data are utilized in semi-supervised
training. Upon evaluation, our method significantly enhances the performance in
the semantic segmentation and geometric reconstruction of pelvic organs, Dice
coefficient can increase by 2.65% averagely. Especially for organs that are
difficult to segment, such as the uterus, the accuracy of semantic segmentation
can be improved by up to 3.70%.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Metacognition-Enhanced Few-Shot Prompting With Positive Reinforcement
Abstract: Few-shot prompting elicits the remarkable abilities of large language models
by equipping them with a few demonstration examples in the input. However, the
traditional method of providing large language models with all demonstration
input-output pairs at once may not effectively guide large language models to
learn the specific input-output mapping relationship. In this paper, inspired
by the regulatory and supportive role of metacognition in students' learning,
we propose a novel metacognition-enhanced few-shot prompting, which guides
large language models to reflect on their thought processes to comprehensively
learn the given demonstration examples. Furthermore, considering that positive
reinforcement can improve students' learning motivation, we introduce positive
reinforcement into our metacognition-enhanced few-shot prompting to promote the
few-shot learning of large language models by providing response-based positive
feedback. The experimental results on two real-world datasets show that our
metacognition-enhanced few-shot prompting with positive reinforcement surpasses
traditional few-shot prompting in classification accuracy and macro F1.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: MaxK-GNN: Towards Theoretical Speed Limits for Accelerating Graph Neural Networks Training
Abstract: In the acceleration of deep neural network training, the GPU has become the
mainstream platform. GPUs face substantial challenges on GNNs, such as workload
imbalance and memory access irregularities, leading to underutilized hardware.
Existing solutions such as PyG, DGL with cuSPARSE, and GNNAdvisor frameworks
partially address these challenges but memory traffic is still significant.
  We argue that drastic performance improvements can only be achieved by the
vertical optimization of algorithm and system innovations, rather than treating
the speedup optimization as an "after-thought" (i.e., (i) given a GNN
algorithm, designing an accelerator, or (ii) given hardware, mainly optimizing
the GNN algorithm). In this paper, we present MaxK-GNN, an advanced
high-performance GPU training system integrating algorithm and system
innovation. (i) We introduce the MaxK nonlinearity and provide a theoretical
analysis of MaxK nonlinearity as a universal approximator, and present the
Compressed Balanced Sparse Row (CBSR) format, designed to store the data and
index of the feature matrix after nonlinearity; (ii) We design a coalescing
enhanced forward computation with row-wise product-based SpGEMM Kernel using
CBSR for input feature matrix fetching and strategic placement of a sparse
output accumulation buffer in shared memory; (iii) We develop an optimized
backward computation with outer product-based and SSpMM Kernel.
  We conduct extensive evaluations of MaxK-GNN and report the end-to-end system
run-time. Experiments show that MaxK-GNN system could approach the theoretical
speedup limit according to Amdahl's law. We achieve comparable accuracy to SOTA
GNNs, but at a significantly increased speed: 3.22/4.24 times speedup (vs.
theoretical limits, 5.52/7.27 times) on Reddit compared to DGL and GNNAdvisor
implementations.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: AI Competitions and Benchmarks: Competition platforms
Abstract: The ecosystem of artificial intelligence competitions is a diverse and
multifaceted landscape, encompassing a variety of platforms that each host
numerous competitions annually, alongside a plethora of specialized websites
dedicated to singular contests. These platforms adeptly manage the overarching
administrative responsibilities inherent in orchestrating competitions, thus
affording organizers the liberty to allocate greater attention to other facets
of their contests. Notably, these platforms exhibit considerable diversity in
their operational functionalities, economic models, and community dynamics.
This chapter conducts an extensive review of the foremost services in this
realm and elucidates several alternative methodologies that facilitate the
independent hosting of such challenges. Keywords: competition platform,
challenge hosting services, comparison.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Efficient In-Context Learning in Vision-Language Models for Egocentric Videos
Abstract: Recent advancements in text-only large language models (LLMs) have
highlighted the benefit of in-context learning for adapting to new tasks with a
few demonstrations. However, extending in-context learning to large
vision-language models (VLMs) using a huge amount of naturalistic
vision-language data has shown limited success, particularly for egocentric
videos, due to high data collection costs. We propose a novel training method
$\mathbb{E}$fficient $\mathbb{I}$n-context $\mathbb{L}$earning on
$\mathbb{E}$gocentric $\mathbb{V}$ideos ($\mathbb{EILEV}$), which elicits
in-context learning in VLMs for egocentric videos without requiring massive,
naturalistic egocentric video datasets. $\mathbb{EILEV}$ involves architectural
and training data adaptations to allow the model to process contexts
interleaved with video clips and narrations, sampling of in-context examples
with clusters of similar verbs and nouns, use of data with skewed marginal
distributions with a long tail of infrequent verbs and nouns, as well as
homonyms and synonyms. Our evaluations show that $\mathbb{EILEV}$-trained
models outperform larger VLMs trained on a huge amount of naturalistic data in
in-context learning. Furthermore, they can generalize to not only
out-of-distribution, but also novel, rare egocentric videos and texts via
in-context learning, demonstrating potential for applications requiring
cost-effective training, and rapid post-deployment adaptability. Our code and
demo are available at \url{https://github.com/yukw777/EILEV}.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models
Abstract: A central component of rational behavior is logical inference: the process of
determining which conclusions follow from a set of premises. Psychologists have
documented several ways in which humans' inferences deviate from the rules of
logic. Do language models, which are trained on text generated by humans,
replicate these biases, or are they able to overcome them? Focusing on the case
of syllogisms -- inferences from two simple premises, which have been studied
extensively in psychology -- we show that larger models are more logical than
smaller ones, and also more logical than humans. At the same time, even the
largest models make systematic errors, some of which mirror human reasoning
biases such as ordering effects and logical fallacies. Overall, we find that
language models mimic the human biases included in their training data, but are
able to overcome them in some cases.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Refining the ONCE Benchmark with Hyperparameter Tuning
Abstract: In response to the growing demand for 3D object detection in applications
such as autonomous driving, robotics, and augmented reality, this work focuses
on the evaluation of semi-supervised learning approaches for point cloud data.
The point cloud representation provides reliable and consistent observations
regardless of lighting conditions, thanks to advances in LiDAR sensors. Data
annotation is of paramount importance in the context of LiDAR applications, and
automating 3D data annotation with semi-supervised methods is a pivotal
challenge that promises to reduce the associated workload and facilitate the
emergence of cost-effective LiDAR solutions. Nevertheless, the task of
semi-supervised learning in the context of unordered point cloud data remains
formidable due to the inherent sparsity and incomplete shapes that hinder the
generation of accurate pseudo-labels. In this study, we consider these
challenges by posing the question: "To what extent does unlabelled data
contribute to the enhancement of model performance?" We show that improvements
from previous semi-supervised methods may not be as profound as previously
thought. Our results suggest that simple grid search hyperparameter tuning
applied to a supervised model can lead to state-of-the-art performance on the
ONCE dataset, while the contribution of unlabelled data appears to be
comparatively less exceptional.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Fine-Grained Image-Text Alignment in Medical Imaging Enables Cyclic Image-Report Generation
Abstract: To address these issues, we propose a novel Adaptive patch-word Matching
(AdaMatch) model to correlate chest X-ray (CXR) image regions with words in
medical reports and apply it to CXR-report generation to provide explainability
for the generation process. AdaMatch exploits the fine-grained relation between
adaptive patches and words to provide explanations of specific image regions
with corresponding words. To capture the abnormal regions of varying sizes and
positions, we introduce the Adaptive Patch extraction (AdaPatch) module to
acquire the adaptive patches for these regions adaptively. In order to provide
explicit explainability for CXR-report generation task, we propose an
AdaMatch-based bidirectional large language model for Cyclic CXR-report
generation (AdaMatch-Cyclic). It employs the AdaMatch to obtain the keywords
for CXR images and `keypatches' for medical reports as hints to guide
CXR-report generation. Extensive experiments on two publicly available CXR
datasets prove the effectiveness of our method and its superior performance to
existing methods.

Output: Computer Vision