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**A**:
The key idea is to transform the diagonal matrix with the help of row and column operations into the identity matrix in a way similar to an algorithm to compute the elementary divisors of an integer matrix, as described for example in [23, Chapter 7, Section 3]**B**: Thus recording the row and and column operations required to transform a diagonal matrix into the identity, allows us to write the input matrix as a product of transvections. **C**: Note that row and column operations are effected by left- and right multiplications by transvections | BAC | ABC | ACB | BCA | Selection 3 |
**A**: The exact definition of some basis functions requires solving global problems, but, based on decaying properties, only local computations are required, although these are not restricted to a single element**B**: It is interesting to notice that, although the formulation is based on hybridization, the final numerical solution is defined by a sequence of elliptic problems.**C**:
As in many multiscale methods previously considered, our starting point is the decomposition of the solution space into fine and coarse spaces that are adapted to the problem of interest | BAC | BCA | CBA | ACB | Selection 2 |
**A**: These coordinates are computed somehow and their true values can differ from their values stored in the computer**B**: Moreover, Alg-A is more stable than the alternatives.
During the iterations of Alg-CM, the coordinates of three corners and two midpoints of a P-stable triangle (see Figure 37) are maintained**C**: Alg-CM uses an involved subroutine (far more complicated than ours given in Algorithm 1) to update the coordinates in each iteration, which accumulates the inaccuracy of coordinates. Even worse, this subroutine computes three angles and selects the smallest to decide how to proceed each time, and due to float issue it is possible to select a wrong angle when angles are close, which causes the subroutine performs incorrectly. | ABC | BAC | BCA | BCA | Selection 2 |
**A**: In the lower part of the pipeline, we extract features from tweets and combine them with the creditscore to construct the feature vector in a time series structure called Dynamic Series Time Model**B**: (non-rumor) news classification.
**C**: These feature vectors are used to train the classifier for rumor vs | BCA | BCA | ACB | ABC | Selection 3 |
**A**: Assumption 1 includes many common loss functions, including the logistic, exp-loss222The exp-loss does not have a global β𝛽\betaitalic_β smoothness parameter**B**: However, if we initialize with η<1/ℒ(𝐰(0))𝜂1ℒ𝐰0\eta<1/\mathcal{L}(\mathbf{w}(0))italic_η < 1 / caligraphic_L ( bold_w ( 0 ) ) then it is straightforward to show the gradient descent iterates maintain bounded local smoothness**C**: and probit losses.
Assumption 1 implies | BAC | ABC | BAC | BCA | Selection 2 |
**A**: It is quite reasonable that the news event would have higher probability to be reported by news or authorized websites. And it is clear to see that their performances significantly improve after 24 hours. But the other original Twitter functions like the retweets or mention do not contribute much.
**B**: The 3 best of Twitter Features are all based on contained URLs in tweets: ContainNEWS, UrlRankIn5000, WotScore, as shown in Table 8**C**: The performance of Twitter features are stable over time from the beginning to the end | BAC | CBA | BCA | BCA | Selection 2 |
**A**: We use the unigram model with default Dirichlet smoothing.
**B**: Language Model-based, how likely aspects are generated by as stastical LM based on the textual representation of the entity 𝖽(e)𝖽𝑒\mathsf{d}(e)sansserif_d ( italic_e )**C**: We model 𝖽(e)𝖽𝑒\mathsf{d}(e)sansserif_d ( italic_e ) as the corresponding Wikipedia article text | BCA | CAB | BCA | CBA | Selection 2 |
**A**: The only difference happens to patient 10 and 12 whose intakes are earlier at day.
Further, patient 12 takse approx**B**: 3 times the average insulin dose of others in the morning.**C**: The insulin intakes tend to be more in the evening, when basal insulin is used by most of the patients | BCA | ACB | BAC | CBA | Selection 1 |
**A**: This development has ultimately led to applying deep neural networks with emergent representations for the estimation of human fixation patterns. Vig et al. (2014) were the first to train an ensemble of shallow CNNs to derive saliency maps from natural images in an end-to-end fashion, but failed to capture object information due to limited network depth.**B**: (2009) introduced a model based on support vector machines to estimate fixation densities from a set of low-, mid-, and high-level visual features. While this approach still relied on a hypothesis specifying which image properties would successfully contribute to the prediction of saliency, it marked the beginning of a progression from manual engineering to automatic learning of features**C**:
With the large-scale acquisition of eye tracking measurements under natural viewing conditions, data-driven machine learning techniques became more practicable. Judd et al | CBA | CAB | ACB | ABC | Selection 1 |
**A**: Each isolated occurrence results in a new marked block, while each block-extending occurrence just extends an already existing marked block, and potentially may even combine two marked blocks and therefore may decrease the overall number of marked blocks. Therefore, marking a symbol when it only has isolated occurrences causes the maximum number of marked blocks that can ever be contributed by this symbol, and therefore this seems to be the worst time to mark this symbol. Hence, in terms of a greedy strategy, it seems reasonable to only mark symbols if they also have block-extending occurrence (obviously, this is not possible for the initially marked symbol).
**B**: In the following, we investigate another aspect of greedy strategies**C**: Any symbol that is marked next in a marking sequence can have isolated occurrences (i. e., occurrences that are not adjacent to any marked block) and block-extending occurrences (i. e., occurrences with at least one adjacent marked symbol) | ABC | CAB | ABC | BCA | Selection 2 |
**A**: Each bar illustrates the number of interactions with environment required by Rainbow (left) or PPO (right) to achieve the same score as our method (SimPLe)**B**:
Figure 3: Comparison with Rainbow and PPO**C**: The red line indicates the 100100100100K interactions threshold which is used by the our method. | BAC | BCA | BCA | BCA | Selection 1 |
**A**: Compared to step negotiation purely in rolling locomotion mode, the proposed strategy demonstrated significant enhancements in energy performance, particularly for taller steps**B**:
The implementation of the energy criterion strategy has proven effective in facilitating autonomous locomotion mode transitions for the Cricket robot when negotiating steps of varying heights**C**: A significant feature of this method is the determination of transition criterion threshold values based on studies of alternative locomotion modes rather than relying on empirical settings. This contribution is crucial as it ensures a more systematic and objective approach to setting the thresholds for locomotion mode transitions. | ABC | CAB | BAC | CBA | Selection 3 |
**A**:
In future work, we would like to expand the model so as to incorporate, into the analysis, the concept of advice error**B**: More specifically, given an advice string of size k𝑘kitalic_k, let η𝜂\etaitalic_η denote the number of erroneous bits (which may be not known to the algorithm)**C**: In this setting, the objective would be to study the power and limitations of online algorithms, i.e., from the point of view of both upper and lower bounds on the competitive ratio. A first approach towards this direction was made recently in the context of problems such as contract | BCA | ABC | BAC | CBA | Selection 2 |
**A**: It is important to note that, as it is described in Section 2.2 of [Losada & Crestani, 2016], to construct the depression group, authors first collected users by doing specific searches on Reddit (e.g**B**: “I was diagnosed with depression”) to obtain self-expressions of depression diagnoses, and then they manually reviewed the matched posts to verify that they were really genuine.
According to the authors, this manual review was strict, expressions like “I have depression”, “I think I have depression”, or “I am depressed” did not qualify as explicit expressions of a diagnosis**C**: They only included a user into the depression group when there was a clear and explicit mention of a diagnosis (e.g., “In 2013, I was diagnosed with depression”, “After struggling with depression for many years, yesterday I was diagnosed”). That introduces the possibility of having some noise in both categories of the collected data, therefore, from now on, when we refer to “depressed” it should be interpreted as “possibly diagnosed with depression”. | CBA | ABC | CBA | BAC | Selection 2 |
**A**: Compared with DGC (w/o mfm), the parameter in GMC converges closer to the optimal point and then remains stable.
Figure 2(a) shows the distances to the global optimal point during the optimization process**B**: We can find that after a sufficient number of iterations, the parameter in DGC (w/o mfm) can only oscillate within a relatively large neighborhood of the optimal point**C**: We can find that although the momentum factor masking trick can make the convergence trajectory appear more stable, it also slows down the convergence. | BAC | BCA | BCA | ACB | Selection 1 |
**A**: operation.**B**:
, where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks**C**: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization | ABC | ACB | CAB | BCA | Selection 3 |
**A**: The higher altitude it is, the larger coverage size a UAV has. A large coverage size means a substantial opportunity of supporting more users, but a higher SNR will be needed**B**: Furthermore, the turbulence of upper air disrupts the stability of UAVs with more energy consumption. Thus, a suitable height is essential to determine the coverage area.**C**:
Coverage is another factor which determines the performance of each UAV. As presented in Fig. 1 (c), the altitude of UAV plays an important role in coverage adjusting | CAB | BCA | ACB | ACB | Selection 2 |
**A**: , superimposition
of the ψcompsubscript𝜓𝑐𝑜𝑚𝑝\psi_{comp}italic_ψ start_POSTSUBSCRIPT italic_c italic_o italic_m italic_p end_POSTSUBSCRIPT boundary conditions, scaled by experimentally**B**: tcomp=45μsubscript𝑡𝑐𝑜𝑚𝑝45μt_{comp}=45\upmuitalic_t start_POSTSUBSCRIPT italic_c italic_o italic_m italic_p end_POSTSUBSCRIPT = 45 roman_μs means that magnetic compression (i.e.,formulae-sequence𝑖𝑒i.e.,italic_i **C**: italic_e | CBA | BAC | BCA | CAB | Selection 4 |
**A**: Our findings indicate that the Dropout-DQN method is effective in decreasing both variance and overestimation**B**: In this study, we proposed and experimentally analyzed the benefits of incorporating the Dropout technique into the DQN algorithm to stabilize training, enhance performance, and reduce variance**C**: However, our experiments were limited to simple problems and environments, utilizing small network architectures and only two Dropout methods.
| CAB | CAB | BAC | CBA | Selection 3 |
**A**: He et al. (2017) extended Faster R-CNN (Ren et al., 2015) by adding a new branch to predict the object mask along with a class label and a bounding box, and the proposed model was called Mask R-CNN. Mask R-CNN has been used extensively for multi-task segmentation models for a wide range of application areas (Abdulla, 2017), such as adding sports fields to OpenStreetMap (Remillard, 2018), detection and segmentation for surgery robots (SUYEgit, 2018), understanding climate change patterns from aerial imagery of the Arctic (Zhang et al., 2018a), converting satellite imagery to maps (Mohanty, 2018), detecting image forgeries (Wang et al., 2019d), and segmenting tree canopy (Zhao et al., 2018).
**B**: Bischke et al**C**: (2019) proposed a cascaded multi-task loss to preserve boundary information from segmentation masks for segmenting building footprints and achieved state-of-the-art performance on an aerial image labeling task | BAC | BAC | CAB | CBA | Selection 3 |
**A**: Generating data with different numbers of decision trees is visualized in the left column**B**: Additionally, a comparison between random sampling (red), NRFI uniform (orange), and NRFI dynamic (green) is shown in the right column.
By optimizing the decision tree sampling, NRFI dynamic automatically balances the confidences and generates the most diverse and evenly distributed data.**C**: Probability distribution of the predicted confidences for different data generation settings on Soybean with 5555 (top) and 50505050 samples per class (bottom) | CBA | BCA | BAC | BAC | Selection 2 |
**A**: As is shown subsequently, solving such a subproblem corresponds to one iteration of infinite-dimensional mirror descent (Nemirovsky and Yudin, 1983) or dual averaging (Xiao, 2010), where the action-value function plays the role of the gradient. To encourage exploration, we explicitly incorporate a bonus function into the action-value function, which quantifies the uncertainty that arises from only observing finite historical data. Through uncertainty quantification, such a bonus function ensures the (conservative) optimism of the updated policy. Based on NPG, TRPO, and PPO, OPPO only augments the action-value function with the bonus function in an additive manner, which makes it easily implementable in practice.
**B**: Our algorithm is also closely related to NPG and TRPO. At each update, OPPO solves a Kullback-Leibler (KL)-regularized policy optimization subproblem, where the linear component of the objective function is defined using the action-value function**C**: To answer this question, we propose the first policy optimization algorithm that incorporates exploration in a principled manner. In detail, we develop an Optimistic variant of the PPO algorithm, namely OPPO | BCA | CAB | ACB | CBA | Selection 4 |
**A**: (2022) which provides an overview of various transformer-based architectures that focus on efficiency, reduced memory-footprint and computational complexity**B**: Sparse attention mechanisms and approximations have been proposed to address this issue and improve the efficiency of transformers for longer sequences.
We refer to the work of Tay et al**C**: Most of these methods focus on the quadratic complexity of the self-attention heads and use low-rank matrix operations, downsampling or exploit pre-set or learned sparsity patterns. | BCA | BAC | BCA | CBA | Selection 2 |
**A**:
The reverse inequality follows from Proposition 9.1 and Remarks 9.2 and 9.13 relating the spread to the filling radius of spheres**B**: Indeed, by basic properties of the bottleneck distance,777The cost of the empty matching upper bounds the bottleneck distance**C**: for every integer k≥0𝑘0k\geq 0italic_k ≥ 0, | CAB | ABC | CAB | CBA | Selection 2 |
**A**: After this time slot ended, no more questions were answered.
The third step was to perform a set of specific tasks described in a handout, using a t-SNE projection of the Breast Cancer Wisconsin data set provided by the tool, and to answer the questions related to these tasks (see Tasks below for details)**B**: In the second step, after watching the video, the participants had a fixed time slot to play with the tool without any specific goal, and to ask questions**C**: Participants were also asked to notify when each task was completed, so we could track the task-specific completion times. | BAC | ACB | ACB | ACB | Selection 1 |
**A**: The other cause reflected by the authors is “the lack of a well-established statistical tradition in the field compounds the problem, leading to generally poor practices by authors and, in many cases, an inability of reviewers to pick up on the main methodological problems of some papers”.
**B**: One cause is the pressure to “publish or perish,” and authors argue that the “publishing metaphor-based method is perceived as a low-effort, low-risk process with high potential rewards” because there are authors that have built professional careers out of creating not one but often multiple metaphor-based methods**C**: In [22], authors discuss the possible causes of the exponential growth of nature-inspired algorithms and the negative consequences for the field | ACB | CBA | BCA | ABC | Selection 2 |
**A**: Since a large proportion of clustering methods are based on the graph, it is reasonable to consider how to employ GCN to promote the performance of graph-based clustering methods.
In this paper, we propose an Adaptive Graph Auto-Encoder (AdaGAE) to extend graph auto-encoder into common scenarios**B**: However, the existing methods are limited to graph type data while no graph is provided for general data clustering**C**: The main contributions are listed as follows: | ABC | BAC | BCA | ACB | Selection 2 |
**A**: Any router along the path whose MTU is smaller than the packet will drop the packet, and send back an ICMP Fragmentation Needed / Packet Too Big (PTB). The payload of the ICMP packet contains the IP header and the first 8 bytes of the original packet that triggered the error as well as the MTU of the router that sent the ICMP message. After receiving an ICMP PTB message, the source host should either reduce its path MTU appropriately or unset the DF bit.**B**:
Path Maximum Transmission Unit Discovery (PMTUD) determines the MTU size on the network path between two IP hosts**C**: The process starts by setting the Don’t Fragment (DF) bit in IP headers | BAC | BAC | CBA | CAB | Selection 4 |
**A**: When something changes, the entire system is taken offline and modified to fit the new situation**B**: It is common to try to avoid such changes in artificial agents, machines, and industrial processes**C**: This process is costly and disruptive; adaptation similar to that in nature might make such systems more reliable and long-term, and thus cheaper to operate.
| ACB | BAC | CBA | CAB | Selection 2 |
**A**: In fact, the construction to generate these semigroups is quite simple [4, Proposition 4.1] (compare also to 3). The same construction can also be used to generate free monoids as automaton semigroups or monoids. Here, the main difference is that the free monoid in one generator can indeed be generated by an automaton: it is generated by the adding machine (see 1), which also generates the free group of rank one if inverses are added. On a side note, it is also worthwhile to point out that – although there does not seem to be much research on the topic – there are examples to generate the free inverse semigroup of rank one as a subsemigroup of an automaton semigroup [14, Theorem 25] and an adaption to present the free inverse monoid of rank one as an automaton semigroup [6, Example 2] (see also [8, Example 23]).**B**:
There is a quite interesting evolution of constructions to present free groups in a self-similar way or even as automaton groups (see [15] for an overview). This culminated in constructions to present free groups of arbitrary rank as automaton groups where the number of states coincides with the rank [18, 17]**C**: While these constructions and the involved proofs are generally deemed quite complicated, the situation for semigroups turns out to be much simpler. While it is known that the free semigroup of rank one is not an automaton semigroup [4, Proposition 4.3], the free semigroups of higher rank can be generated by an automaton [4, Proposition 4.1] | ABC | BCA | BCA | CAB | Selection 4 |
**A**: Table A4 shows VQA accuracy for each answer type on VQACPv2’s test set. HINT/SCR and our regularizer show large gains in ‘Yes/No’ questions**B**: We hypothesize that the methods help forget linguistic priors, which improves test accuracy of such questions. In the train set of VQACPv2, the answer ‘no’ is more frequent than the answer ‘yes’, tempting the baseline model to answer ‘yes/no’ questions with ‘no’**C**: However, in the test set, answer ‘yes’ is more frequent. Regularization effects caused by HINT/SCR and our method cause the models to weaken this prior i.e., reduce the tendency to just predict ‘no’, which would increase accuracy at test because ‘yes’ is more frequent in the test set. Next, all of the methods perform poorly on ‘Number (Num)’ answer type, showing that methods find it difficult to answer questions that are most reliant on correct visual grounding such as: localizing and counting objects. Finally, we do not observe large improvements in ‘Other’ question type, most likely due to the large number of answers present under this answer type.
| CAB | ABC | CBA | ACB | Selection 2 |
**A**: Since Roberta accepts a maximum of 512 tokens as input, only the first 512 tokens of text from the documents were used for training while the rest was discarded. As shown in the analysis section, the average length of a privacy policy in terms of the number of words is 1,871. Thus 512 tokens would take into account about a fourth of an average privacy policy.
**B**: We used the pretrained RoBERTa tokenizer to tokenize text extracted from the documents**C**: To train the RoBERTa model on the privacy policy classification task, we used the sequence classification head of the pretrained language model from HuggingFace (Wolf et al., 2019) | BCA | CAB | CAB | CBA | Selection 4 |
**A**: The authors studied the automatic detection of seven stance categories: certainty, uncertainty, hypotheticality, prediction, recommendation, concession/contrast, and source**B**: Their model performed best for the hypotheticality category, using a baseline classification approach without the application of heavy feature selection/engineering, therefore we focus on this category in our comparison. It can be considered as a binary classification problem: the presence or absence of hypotheticality.**C**:
In this section, we describe how StackGenVis can be used to improve the results of sentiment/stance detection in texts from social media, when compared to previous work from Skeppstedt et al. [51] | BCA | CBA | CBA | ABC | Selection 1 |
**A**: Task similarity. In Persona and Weibo, each task is a set of dialogues for one user, so tasks are different from each other. We shuffle the samples and randomly divide tasks to construct the setting that tasks are similar to each other**B**: For a fair comparison, each task on this setting also has 120 and 1200 utterances on average in Persona and Weibo respectively. We train and evaluate Transformer-F and MAML on this setting. (Table 2).
When tasks are similar to each other, MAML performs comparatively poorly**C**: In Persona and Weibo, the performance of MAML is similar to that of Transformer-F, while MAML performs significantly better than Transformer-F when tasks are different. A possible explanation is that if there is no clear distinction between tasks, the meta-learning setting can be viewed as a transfer learning setting, which only has a source domain and a target domain, and fine-tuning performs well in transfer learning. So if the tasks are similar to each other, we can simply use Transformer-F rather than MAML. | BCA | BAC | ABC | BAC | Selection 3 |
**A**: Noting the interdependent relationship between the beamformer/combiner (or AWV) and the activated subarray or subarray partition, a well-structured codebook should be designed to facilitate the fast localization of the activated subarray and flexible beam control**B**: For this goal, the CCA codebook design and the codebook-based joint Subarray Partition and Array-weighting-vector Selection (SPAS) algorithm will be first proposed in the next section.**C**: ℱℱ\mathcal{F}caligraphic_F and 𝒲𝒲\mathcal{W}caligraphic_W are the sets of all analog beamforming vectors and combing vectors satisfying the hardware constraints, respectively.
In fact, solving the above problem (13) requires the new codebook design and codeword selection/processing strategy | CBA | BCA | CAB | BAC | Selection 2 |
**A**: We**B**: Note that the 1111-color case with the completeness requirement is not very interesting, and also not useful for the general case: completeness states that every node on
the left must be connected, via the unique edge relation, to every node on the right – regardless of the matrix**C**: This will be bootstrapped to the multi-color case in later sections | CAB | ACB | ACB | CBA | Selection 4 |
**A**: Thus, their analysis is not directly applicable to our setting. We defer the detailed discussion on the approximation analysis to §B. Proposition 3.1 allows us to convert the TD dynamics over the finite-dimensional parameter space to its counterpart over the infinite-dimensional Wasserstein space, where the infinitely wide neural network Q(⋅;ρ)𝑄⋅𝜌Q(\cdot;\rho)italic_Q ( ⋅ ; italic_ρ ) in (3.2) is linear in the distribution ρ𝜌\rhoitalic_ρ.**B**: The proof of Proposition 3.1 is based on the propagation of chaos (Sznitman, 1991; Mei et al., 2018, 2019).
In contrast to Mei et al**C**: (2018, 2019), the PDE in (3.4) can not be cast as a gradient flow, since there does not exist a corresponding energy functional | BCA | CAB | BCA | BCA | Selection 2 |
**A**: 2) Sharing parameters for the computation of gates (Equations 2, 3, 4) leads to slightly higher BLEU with fewer parameters introduced than without sharing them (“None” in Table 5)**B**:
Table 5 shows that: 1) Sharing parameters for the computation (Equation 6) of the depth-wise LSTM hidden state significantly hampers performance, which is consistent with our conjecture**C**: Thus, in the other experiments, we bind parameters for the computation of LSTM gates across stacked layers by default. | CAB | CBA | CBA | BAC | Selection 4 |
**A**: \prime},y^{\prime})}\subseteq f^{-1}(U)( italic_a start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT , italic_y start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ) ∈ italic_V start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT ( italic_a start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT , italic_y start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ) end_POSTSUPERSCRIPT × italic_V start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT ( italic_a start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT , italic_y start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ) end_POSTSUPERSCRIPT ⊆ italic_f start_POSTSUPERSCRIPT - 1 end_POSTSUPERSCRIPT ( italic_U )**B**: In particular, as
b∈W⊆V2(a′,y′)𝑏𝑊superscriptsubscript𝑉2superscript𝑎′superscript𝑦′b\in W\subseteq V_{2}^{(a^{\prime},y^{\prime})}italic_b ∈ italic_W ⊆ italic_V start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT ( italic_a start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT , italic_y start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ) end_POSTSUPERSCRIPT, we conclude that (a′,b)∈f−1(U)superscript𝑎′𝑏superscript𝑓1𝑈(a^{\prime},b)\in f^{-1}(U)( italic_a start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT , italic_b ) ∈ italic_f start_POSTSUPERSCRIPT - 1 end_POSTSUPERSCRIPT ( italic_U )**C**: Because a′≡1asubscript1superscript𝑎′𝑎a^{\prime}\equiv_{1}aitalic_a start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT ≡ start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT italic_a, this proves that | BAC | ABC | CBA | CBA | Selection 2 |
**A**:
1**B**: Thus, our learning model gains sufficient distortion perception of features and shows faster convergence. Moreover, this representation enables more efficient learning with less data required.**C**: The proposed ordinal distortion is a learning-friendly representation for neural networks, which is explicit and homogeneous compared with the implicit and heterogeneous distortion parameters | CAB | ACB | CAB | BCA | Selection 2 |
**A**: As recommended in [32], we use warm-up and polynomial learning rate strategy.**B**: We train the model with 90 epochs**C**:
To further verify the superiority of SNGM with respect to LARS, we also evaluate them on a larger dataset ImageNet [2] and a larger model ResNet50 [10] | CAB | CBA | ABC | BAC | Selection 2 |
**A**:
Unfortunately, standard SAA approaches [26, 7] do not directly apply to radius minimization problems**B**: On a high level, the obstacle is that radius-minimization requires estimating the cost of each approximate solution; counter-intuitively, this may be harder than optimizing the cost (which is what is done in previous results)**C**: See Appendix A for an in-depth discussion. | BAC | ACB | ABC | ACB | Selection 3 |
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