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Of course, the numerical scheme and the estimates developed in Section 3.1 hold. <|MaskedSetence|> We remark that in this case, our method is similar to that of [MR3591945], with some differences. <|MaskedSetence|> <|MaskedSetence|> We had to reconsider the proofs, in our view simplifying some of them.
. | **A**: However, several simplifications are possible when the coefficients have low-contrast, leading to sharper estimates.
**B**: First we consider that T~~𝑇\tilde{T}over~ start_ARG italic_T end_ARG can be nonzero.
**C**: Also, our scheme is defined by a sequence of elliptic problems, avoiding the annoyance of sadd... | ABC | ABC | BAC | ABC | Selection 4 |
We observe that at certain points in time, the volume of rumor-related tweets (for sub-events) in the event stream surges. This can lead to false positives for techniques that model events as the aggregation of all tweet contents; that is undesired at critical moments. We trade-off this by debunking at single tweet le... | **A**: We can see the curve of Munich shooting event is also close to the curve of average news, indicating the event is more news-related..
**B**: In addition, we show the feature analysis for ContainNews (percentage of URLs containing news websites) for the event Munich shooting in Figure 5(b).
**C**: We show the C... | BCA | CBA | CBA | CBA | Selection 2 |
CrowdWisdom. <|MaskedSetence|> For this purpose, (liu2015real, ) use an extensive list of bipolar sentiments with a set of combinational rules. <|MaskedSetence|> We simplified and generalized the “dictionary” by keeping only a set of carefully curated negative words. <|MaskedSetence|> Our intuition is, that the att... | **A**: Similar to (liu2015real, ), the core idea is to leverage the public’s common sense for rumor detection: If there are more people denying or doubting the truth of an event, this event is more likely to be a rumor.
**B**: We call them “debunking words” e.g., hoax, rumor or not true.
**C**: In contrast to mere se... | ACB | ABC | ACB | ACB | Selection 4 |
Multi-Criteria Learning. <|MaskedSetence|> <|MaskedSetence|> The goal of RankSVM is learning a linear model that minimizes the number of discordant pairs in the training data. <|MaskedSetence|> The temporal and type-dependent ranking model is learned by minimizing the following objective function:
. | **A**: We modified the objective function of RankSVM following our global loss function, which takes into account the temporal feature specificities of event entities.
**B**: We adapted the L2R RankSVM [12].
**C**: Our task is to minimize the global relevance loss function, which evaluates the overall training error,... | CBA | CBA | CBA | CBA | Selection 4 |
Table 1 shows basic patient information. Half of the patients are female and ages range from 17 to 66, with a mean age of 41.8 years. Body weight, according to BMI, is normal for half of the patients, four are overweight and one is obese. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> | **A**: The mean BMI value is 26.9.
**B**: Only one of the patients suffers from diabetes type 2 and all are in ICT therapy.
**C**: In terms of time since being diagnosed with diabetes, patients vary from inexperienced (2 years) to very experienced (35 years), with a mean value of 13.9 years..
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<|MaskedSetence|> (1998). <|MaskedSetence|> All image examples demonstrate a qualitative agreement of our model with the ground truth data, assigning high saliency to regions that contain semantic information, such as a door (a), flower (b), face (c), or text (d). On the contrary, the approach by Itti et al. <|Maske... | **A**: The network proposed in this study was not trained on the stimuli shown here and thus exhibits its generalization ability to unseen instances.
**B**:
Figure 1: A visualization of four natural images with the corresponding empirical fixation maps, our model predictions, and estimated maps based on the work by I... | BAC | BAC | BAC | ACB | Selection 1 |
Our strongest positive result about the approximation of the locality number will be derived from the reduction mentioned above (see Section 5.2). <|MaskedSetence|> This is mainly motivated by two aspects. Firstly, ruling out simple strategies is a natural initial step in the search for approximation algorithms for a... | **A**: This may provide a new angle to approximating the cutwidth of a graph, i.e., some greedy strategies may only become apparent in the locality number point of view and are hard to see in the graph formulation of the problem.
**B**: However, we shall first investigate in Section 5.1 the approximation performance o... | BAC | BAC | BAC | ABC | Selection 1 |
Although prior works have proposed training predictive models for next-frame, future-frame, as well as combined future-frame and reward predictions in Atari games (Oh et al. <|MaskedSetence|> (2017); Leibfried et al. (2016)), no prior work has successfully demonstrated model-based control via predictive models that a... | **A**: (2018)) this was formulated as the following challenge: “So far, there has been no clear demonstration of successful planning with a learned model in the ALE”..
**B**: (2015); Chiappa et al.
**C**: Indeed, in a recent survey (Section 7.2 in Machado et al.
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During the step negotiation simulations, it was noticed that the rolling locomotion mode encountered constraints when attempting to cross steps with a height greater than thrice the track height (h being the track height as shown in Fig. <|MaskedSetence|> <|MaskedSetence|> As a result, successful locomotion mode tra... | **A**: This limitation originates from the traction forces generated by the tracks.
**B**: 3).
**C**: For evaluating the energy expenditure during step negotiation, energy assessments were carried out for step heights of h, 2h, and 3h using both the whole body climbing and rear body climbing gaits and shown in Fig.
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It should be fairly clear that such assumptions are very unrealistic or undesirable. <|MaskedSetence|> In addition, the known advice models often allow
information that one may arguably consider unrealistic, e.g., an encoding of some part of the offline optimal solution. Last, and perhaps more significantly, a malicio... | **A**: In the traditional advice model, one bit suffices to be optimal: 0 for renting throughout the horizon, 1 for buying right away.
**B**: In contrast, an online algorithm that does not use advice at all has competitive ratio at most 2, i.e., its output can be at most twice as costly as the optimal one..
**C**: Ad... | CAB | CAB | CAB | BCA | Selection 3 |
It is worth noting that the difference in terms of space complexity is also very significant. <|MaskedSetence|> For instance, when using SS3 we only need to store the confidence vector303030In case of ADD, a 2-dimensional vector. <|MaskedSetence|> However, when working with classifiers not supporting incremental cla... | **A**: Note that storing either all the documents or a d×t𝑑𝑡d\times titalic_d × italic_t document-term matrix, where d𝑑ditalic_d is the number of documents and t𝑡titalic_t the vocabulary size, takes up much more space than a small 2-dimensional vector..
**B**: For classifiers supporting incremental classification,... | BCA | BCA | BCA | BCA | Selection 4 |
Fig. 12 shows how the number of UAVs affect the computation complexity of SPBLLA. Since the total number of UAVs is diverse, the goal functions are different. The goal functions’ value in the optimum states increase with the growth in UAVs’ number. <|MaskedSetence|> Moreover, more UAVs can cover more area and support... | **A**: Fig. 12 also shows how many iterations that UAV ad-hoc network needs to approach to convergence.
**B**: With the number of UAVs improves, more iterations are required in this network..
**C**: Since goal functions are the summation function of utility functions, more UAVs offer more utilities which result in hi... | BCA | CAB | CAB | CAB | Selection 2 |
Deep neural networks are the state of the art learning models used in artificial intelligence. The large number of parameters in neural networks make them very good at modelling and approximating any arbitrary function. However the larger number of parameters also make them particularly prone to over-fitting, requirin... | **A**: They include variational Dropout[15], Max-pooling Dropout[16], fast Dropout[17], Cutout[18], Monte Carlo Dropout[19], Concrete Dropout[20] and many others..
**B**: Dropout was first introduced in 2012 as a regularization technique to avoid over-fitting[12], and was applied in the winning submission for the Larg... | CAB | BCA | BCA | BCA | Selection 2 |
<|MaskedSetence|> (2017) extended the VGG16 architecture (Simonyan and Zisserman, 2014) to include a global average pooling layer for patient detection and a fully convolutional network for skin segmentation. <|MaskedSetence|> (2019) trained a U-Net (Ronneberger et al., 2015)-like encoder-decoder architecture to simu... | **A**:
Chaichulee et al.
**B**: The proposed model was evaluated on images from a clinical study conducted at a neonatal intensive care unit, and was robust to changes in lighting, skin tone, and pose. He et al.
**C**: (2019) trained a multi-task U-Net architecture to solve three tasks - separating wrongly connected... | ABC | ABC | BCA | ABC | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> (2017) demonstrate that deep neural networks are capable of fitting random labels and memorizing the training data. <|MaskedSetence|> (2020) analyze the performance across different dataset sizes.
Olson et al. (2018) evaluate the performance of modern neural networks using the sam... | **A**: The generalization performance has been widely studied.
**B**: Zhang et al.
**C**: Bornschein et al.
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<|MaskedSetence|> In detail, we develop an Optimistic variant of the PPO algorithm, namely OPPO. <|MaskedSetence|> 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. As is show... | **A**: To answer this question, we propose the first policy optimization algorithm that incorporates exploration in a principled manner.
**B**: Through uncertainty quantification, such a bonus function ensures the (conservative) optimism of the updated policy.
**C**: Our algorithm is also closely related to NPG and T... | ACB | ACB | ABC | ACB | Selection 4 |
We thank Prof. <|MaskedSetence|> Johnathan Bush for very useful feedback about a previous version of this article. We also thank Prof. Mikhail Katz and Prof. Michael Lesnick for explaining to us some aspects of their work. We thank Dr. <|MaskedSetence|> Finally, we thank Dr. <|MaskedSetence|> | **A**: Qingsong Wang for bringing to our attention the paper [76] which was critical for the proof of Theorem 1.
**B**: Alexey Balitsky for pointing out an imprecision in the statement of Proposition 9.2.
.
**C**: Henry Adams and Dr.
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<|MaskedSetence|> She interactively draws a polyline with her mouse following the pattern from the benign cases to the malignant ones, as shown in Figure 6(c). <|MaskedSetence|> She validates her hypothesis by clicking on the “mitoses” dimension and observing that the actual dimension values look almost randomly dist... | **A**: Anna uses the Dimension Correlation in order to determine the role of the data set’s dimensions in the outcome of the projection.
**B**: For this new investigation, she is only interested in the highest correlations, so she sets a threshold for a minimum of 20% for a correlation to be visible.
**C**: By lookin... | ACB | ACB | CAB | ACB | Selection 2 |
<|MaskedSetence|> To begin with, in Table 32 the most influential algorithm was identified to be PSO, appearing in 11% of the reviewed literature (which corresponds to almost 47% of the proposals that were clearly based on a previous algorithm). <|MaskedSetence|> The simplicity of this algorithm and its ability to re... | **A**: Thus, many algorithms whose authors claim to simulate the behavior of a biological system eventually perform their search process through movements strongly influenced by PSO (in some cases, without any significant difference)..
**B**:
Very insightful conclusions can be drawn from this grouping.
**C**: This b... | BCA | BCA | BCA | ACB | Selection 1 |
Classical clustering models work poorly on large scale datasets. <|MaskedSetence|> <|MaskedSetence|> If the graph is not updated, the contained information is low-level. <|MaskedSetence|> In particular, AdaGAE is stable on all datasets.
. | **A**: The adaptive learning will induce the model to exploit the high-level information.
**B**: Although GAE-based models (GAE, MGAE, and GALA) achieve impressive results on graph type datasets, they fail on the general datasets, which is probably caused by the fact that the graph is constructed by an algorithm rathe... | CBA | ACB | CBA | CBA | Selection 4 |
SMap consists of the orchestrator which coordinates and synchronises the prober hosts. <|MaskedSetence|> <|MaskedSetence|> SMap applies one test at a time for each AS in the dataset. <|MaskedSetence|> On the other hand, a failed test may indicate that one of the ASes on the path between the probers and the service ... | **A**: The prober hosts receive the dataset of networks to be scanned for spoofability from the orchestrator.
**B**: Each successful test indicates that packets from a spoofed IP address reached the destination on the target network, implying that the target AS does not filter spoofed packets.
**C**: The probers then... | ACB | ACB | ACB | ACB | Selection 2 |
This paper builds upon previous work with this dataset [7], which used support vector machine (SVM) ensembles. <|MaskedSetence|> <|MaskedSetence|> The context model has two parts: (1) a recurrent context layer, which encodes classification-relevant properties of previously seen data, and (2) a feedforward layer, whic... | **A**: First, their approach is extended to a modern version of feedforward artificial neural networks (NNs) [8].
**B**: Thus, emulation of adaptation in natural systems leads to an approach that can make a difference in real-world applications.
.
**C**: Context-based learning is then introduced to utilize sequential... | BCA | ACB | ACB | ACB | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> While these constructions and the involved proofs are generally deemed quite complicated, the situation for semigroups turns out to be much simpler. <|MaskedSetence|> In fact, the construction to generate these semigroups is quite simple [4, Proposition 4.1] (compare also to 3). T... | **A**: 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].
**B**: This culminated in constructions to present free groups of arbitrary rank as automaton groups where the numbe... | BAC | CBA | CBA | CBA | Selection 3 |
While our results indicate that current visual grounding based bias mitigation approaches do not suffice, we believe this is still a good research direction. <|MaskedSetence|> We recommend that both train and test accuracy be reported, because a model truly capable of visual grounding would not cause drastic drops in ... | **A**: (2017), enabling the community to evaluate if their methods are able to focus on relevant information.
**B**: However, future methods must seek to verify that performance gains are not stemming from spurious sources by using an experimental setup similar to that presented in this paper.
**C**: (2019b); Hudson ... | BCA | BCA | BCA | CBA | Selection 3 |
<|MaskedSetence|> (2016). The OPP-115 Corpus contains manual annotations of 23K fine-grained data practices on 115 privacy policies annotated by legal experts. To the best of our knowledge, this is the most detailed and widely used dataset of annotated privacy policies in the research community. <|MaskedSetence|> <|... | **A**: The OPP-115 Corpus contains paragraph-sized segments annotated according to one or more of the twelve coarse-grained categories of data practices.
**B**:
For the data practice classification task, we leveraged the OPP-115 Corpus introduced by Wilson et al.
**C**: We fine-tuned PrivBERT on the OPP-115 Corpus t... | BAC | CBA | BAC | BAC | Selection 4 |
In this paper, we introduced an interactive VA system, called StackGenVis, for the alignment of data, algorithms, and models in stacking ensemble learning. <|MaskedSetence|> <|MaskedSetence|> Exploring the algorithms, the data, and the models from different perspectives and tracking the training process enables user... | **A**: The adaptation of an already-existing knowledge generation model leads us to stable design goals and analytical tasks that were realized by StackGenVis.
**B**: With the careful selection of multiple coordinated views, we allow users to build an effective stacking ensemble from scratch.
**C**: To retrieve preli... | ABC | ABC | BAC | ABC | Selection 1 |
Task similarity. <|MaskedSetence|> <|MaskedSetence|> For a fair comparison, each task on this setting also has 120 and 1200 utterances on average in Persona and Weibo respectively. <|MaskedSetence|> (Table 2).
When tasks are similar to each other, MAML performs comparatively poorly. In Persona and Weibo, the perform... | **A**: We train and evaluate Transformer-F and MAML on this setting.
**B**: In Persona and Weibo, each task is a set of dialogues for one user, so tasks are different from each other.
**C**: We shuffle the samples and randomly divide tasks to construct the setting that tasks are similar to each other.
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<|MaskedSetence|> MmWave band has abundant spectrum resource, and is considered as a potential avenue to support high-throughput data transmission for UAV networks [9, 10, 7]. If the Line-of-Sight (LoS) propagation is available, mmWave communication can achieve kilometer-level communication range and gigabits-persecon... | **A**: The uniform linear array (ULA) and uniform planar array (UPA) are widely adopted in the existing studies on mmWave communication and networking [12, 13, 14, 15].
**B**: In such mission-driven UAV networks, high-data-rate inter-UAV communications play a pivotal role.
**C**: Specifically, a UAV maintains three-d... | BCA | BCA | BAC | BCA | Selection 1 |
Meanwhile, our analysis is related to the recent breakthrough in the mean-field analysis of stochastic gradient descent (SGD) for the supervised learning of an overparameterized two-layer neural network (Chizat and Bach, 2018b; Mei et al., 2018, 2019; Javanmard et al., 2019; Wei et al., 2019; Fang et al., 2019a, b; Che... | **A**: In contrast, TD follows the stochastic semigradient of the MSPBE (Sutton and Barto, 2018), which is biased.
**B**: As a result, there does not exist an energy functional for casting TD as its Wasserstein gradient flow.
**C**: See also the previous analysis in the NTK regime (Daniely, 2017; Chizat and Bach, 201... | BAC | CAB | CAB | CAB | Selection 2 |
It is a common problem that increasing the depth does not always lead to better performance, whether with residual connections Li et al. (2022b) or other previous studies on deep Transformers Bapna et al. <|MaskedSetence|> (2019); Li et al. <|MaskedSetence|> <|MaskedSetence|> As shown in Table 6, the 12-layer Transf... | **A**: (2022a), and the use of wider models is the usual method of choice for further improvements.
**B**: (2018); Wang et al.
**C**: Although for the Base Transformer model our approach does not lead to significant improvements for models deeper than 18181818 layers, we argue that the 18-layer Transformer Base is no... | BAC | BAC | BAC | ABC | Selection 1 |
To exhibit the performance fairly, we employ three common network architectures VGG16, ResNet50, and InceptionV3 as the backbone networks of the learning model. The proposed MDLD metric is used to express the distortion estimation error due to its unique and fair measurement for distortion distribution. <|MaskedSeten... | **A**: From these learning evaluations, we can observe:.
**B**: 6, which is sampled with 20%, 40%, 60%, 80%, and 100% from the entire training data.
**C**: To be specific, we visualize the error and convergence epoch when estimating two representations under the same number of training data in Fig.
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Clustering is a fundamental task in unsupervised and self-supervised learning. <|MaskedSetence|> The black-box model is motivated by data-driven applications where specific knowledge of the distribution is unknown but we have the ability to sample or simulate from the distribution. To our knowledge, radius minimizatio... | **A**: The stochastic setting models situations in which decisions must be made in the presence of uncertainty and are of particular interest in learning and data science.
**B**: 2S-Sup is the natural two-stage counterpart of the well-known Knapsack-Supplier problem, which has a well-known 3333-approximation [14].
.
... | ACB | BAC | ACB | ACB | Selection 4 |
III. The co-existence of random graphs, subgradient measurement noises, additive and multiplicative communication noises are considered. <|MaskedSetence|> What’s more, multiplicative noises relying on the relative states between adjacent local optimizers make states, graphs and noises coupled together. It becomes mor... | **A**: We firstly employ the property of conditional independence to deal with the coupling term of different random factors.
**B**: Compared with the case with only a single random factor, the coupling terms of different random factors inevitably affect the mean square difference between optimizers’ states and any gi... | BAC | BAC | BAC | BAC | Selection 4 |
Although the generalization for k𝑘kitalic_k-anonymity provides enough protection for identities, it is vulnerable to the attribute disclosure [23]. <|MaskedSetence|> <|MaskedSetence|> To prevent such disclosure, many effective principles have been proposed, such as l𝑙litalic_l-diversity [23] and t𝑡titalic_t-close... | **A**: For instance, in Figure 1(b), the sensitive values in the third equivalence group are both “pneumonia”.
**B**: Therefore, an adversary can infer the disease value of Dave by matching his age without re-identifying his exact record.
**C**: Thus, for any individual, the adversary has to obtain at least five diff... | ACB | ABC | ABC | ABC | Selection 3 |
<|MaskedSetence|> MaskRCNN-ResNet50 is used as baseline and it achieves 53.2 mAP. <|MaskedSetence|> (2020) except for extracting both coarse and fine-grained features from the P2-P5 levels of FPN, rather than only P2 described in the paper. Surprisingly, PointRend yields 62.9 mAP and surpasses MaskRCNN by a remarkabl... | **A**: Bells and Whistles.
**B**: X101-64x4d Xie et al.
**C**: For PointRend, we follow the same setting as Kirillov et al.
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<|MaskedSetence|> The reason is that the algorithms designed to explore in stationary MDPs are generally insensitive to abrupt change in the environment. <|MaskedSetence|> On the other hand, in gradually-changing environment, LSVI-UCB and Epsilon-Greedy can perform well in the beginning when the drift of environment ... | **A**: However, when the change of environment is greater, they no longer yield satisfactory performance since their Q𝑄Qitalic_Q function estimate is quite off.
**B**: For example, UCB-type exploration does not have incentive to take actions other than the one with the largest upper confidence bound of Q𝑄Qitalic_Q-v... | CBA | BCA | CBA | CBA | Selection 4 |
Many studies worldwide have observed the proliferation of fake news on social media and instant messaging apps, with social media being the more commonly studied medium. <|MaskedSetence|> Most respondents encountered fake news on instant messaging apps compared to social media, and have reported the least trust in the... | **A**: These suggest that, in Singapore, communication with personal contacts such as through the forwarding of messages, rather than with the public such as by sharing posts on social media feeds, is the larger issue.
**B**: In Singapore, however, mitigation efforts on fake news in instant messaging apps may be more ... | BAC | CAB | BAC | BAC | Selection 3 |
<|MaskedSetence|> As discussed in previous sections, decentRL does not use the embedding of the central entity as input when generating its output embedding. However, this input embedding can still accumulate knowledge by participating in the aggregations of its neighbors. The acquired information may not necessarily ... | **A**: Additionally, decentRL benefits from concatenating the embeddings from multiple layers.
**B**:
The performance of decentRL at the input layer notably lags behind that of other layers and AliNet.
**C**: The optimal performance is achieved by a four-layer decentRL..
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In this work, we consider self-supervised exploration without extrinsic reward. In such a case, the above trade-off narrows down to a pure exploration problem, aiming at efficiently accumulating information from the environment. Previous self-supervised exploration typically utilizes ‘curiosity’ based on prediction-err... | **A**: We focus on developing the pure-exploratory agent and leave the study of policy combination in the future.
.
**B**: MuleX [29] learns several policies independently and uses a random heuristic to decide which one to use in each time step.
**C**: Such policy combination methods perform better than the policy ob... | BCA | BCA | BCA | ACB | Selection 1 |
<|MaskedSetence|> The underlying assumption is that the latent variables H𝐻Hitalic_H can be partitioned into independent components C𝐶Citalic_C (i.e. the disentangled factors) and correlated components Z𝑍Zitalic_Z, a.k.a as nuisance variables, which encode the details information not stored in the independent compo... | **A**: They aren’t really separating into nuisance and independent only..
**B**: they are also throwing away nuisance..
**C**: Prior work in unsupervised DR learning suggests the objective of learning statistically independent factors of the latent space as means to obtain DR.
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Now, we will define ‘window operators’ to have the same connection as a 3-pin based structural computer using the reverse signal pair described earlier. ‘Window operator’ is a cube of 3x3, each containing elements of 0,i,1,-1,2, and 2. Each element (or cell) is inputted in the same way as three pin structural computing... | **A**: i, Black body: absorbs the light from the top to the left.
**B**: 2, forward half-mirror: A translucent object with a 45 degree gradient, some light is transmittable, some reflective.
**C**: Based on the above functions, the window operator is designed to allow the same operation of the connected state as the... | ABC | ABC | ABC | CBA | Selection 2 |
In this article we investigated how different view-selecting meta-learners affect the performance of multi-view stacking. In our simulations, the interpolating predictor often performed worse than the other meta-learners on at least one outcome measure. <|MaskedSetence|> When the sample size was smaller than the numbe... | **A**: For example, when the sample size was larger than the number of views, the interpolating predictor often had the lowest TPR in view selection, as well as the lowest test accuracy, particularly when there was no correlation between the different views.
**B**: However, in both cases it produced very dense models,... | ABC | CAB | ABC | ABC | Selection 1 |
For Phase 1, five feature selection methods, including 2 causal and 3 non-causal methods, are used in our experiments. FBED and HITON-PC are causal feature selection techniques. <|MaskedSetence|> MI, IEPC and DA are non-causal feature selection methods. MI is a mutual information-based feature selection method. <|Mas... | **A**: FBED is used for MB (Markov Blanket) discovery and HITON-PC is for PC (Parents and Children) selection.
**B**: IEPC and DC are consistency-based and dependency-based methods, respectively.
**C**: The slope thresholds in the three techniques for selecting features are all set to 0.8, as recommended by the packa... | ABC | ABC | ABC | BCA | Selection 3 |
CB-MNL enforces optimism via an optimistic parameter search (e.g. in Abbasi-Yadkori et al. <|MaskedSetence|> [2020], Filippi et al. <|MaskedSetence|> Optimistic parameter search provides a cleaner description of the learning strategy. In non-linear reward models, both approaches may not follow similar trajectory but... | **A**: [2010].
**B**: [2011]), which is in contrast to the use of an exploration bonus as seen in Faury et al.
**C**: [2010] for a short discussion)..
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Though many methods (e.g., [1, 3, 9, 20, 21, 24, 42, 43, 44, 46]) in recent years have been continuously breaking the record of TAL performance, a major challenge hinders its substantial improvement – large variation in action duration. An action can last from a fraction of a second to minutes in the real-world scenari... | **A**: Therefore, the accuracy of short actions is a key factor to determine the performance of a TAL method.
.
**B**: We notice that actions shorter than 30 seconds dominate the distribution, but their performance is obviously inferior to longer ones with all different TAL methods (Fig.
**C**: 1 b)).
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<|MaskedSetence|> HyperTuner [LCW∗18] is an interactive VA system that enables hyperparameter search by using a multi-class confusion matrix for summarizing the predictions and setting user-defined ranges for multiple validation metrics to filter out and evaluate the hyperparameters. <|MaskedSetence|> However, this c... | **A**: HyperTendril [PNKC21] is a visualization tool that supports random search, population-based training [JDO∗17], Bayesian optimization, HyperBand [LJD∗17], and the last two methods joined together [FKH18].
**B**: One common focus of related work is the hyperparameter search for deep learning models.
**C**: Users... | BCA | BCA | BCA | CAB | Selection 1 |
<|MaskedSetence|> Our main idea is to simultaneously solve for shape-to-universe matchings and shape-to-universe functional maps. <|MaskedSetence|> This contrasts the recent ConsistentZoomOut [31] method, which does not obtain cycle-consistent multi-matchings. <|MaskedSetence|> Experimentally we have demonstrated th... | **A**: Our algorithm is efficient, straightforward to implement, and montonically increases the objective function.
**B**: By doing so, we generalise the popular functional map framework to multi-matching, while guaranteeing cycle consistency, both for the shape-to-universe matchings, as well as for the shape-to-unive... | BAC | CBA | CBA | CBA | Selection 4 |
<|MaskedSetence|> This characterization decomposes the input graph G𝐺Gitalic_G by clique separators as in [18], then at the recursive step one has to find a proper vertex coloring of an antipodality graph satisfying some particular conditions; see Section 3, in particular Theorem 6. In a few words, an antipodality gr... | **A**:
The recognition algorithm RecognizePG for path graph is mainly built on path graphs’ characterization in [1].
**B**: This order allows us to establish all the antipodality relations in a faster time.
**C**: This is done in Step 4, Step 5, and Step 6 that are the core of algorithm RecognizePG..
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The stochastic blockmodel (SBM) (SBM, ) is one of the most used models for community detection in which all nodes in the same community are assumed to have equal expected degrees. Some recent developments of SBM can be found in (abbe2017community, ) and references therein. Since in empirical network data sets, the deg... | **A**: DCMM model allows that nodes for the same communities have different degrees and some nodes could belong to two or more communities, thus it is more realistic and flexible.
**B**: In this paper, we design community detection algorithms based on the DCMM model..
**C**: MMSB constructed a mixed membership stoch... | CBA | CAB | CAB | CAB | Selection 4 |
See, e.g., Welling and Teh (2011); Chen et al. (2014); Ma et al. (2015); Chen et al. (2015); Dubey et al. (2016); Vollmer et al. <|MaskedSetence|> (2016); Dalalyan (2017); Chen et al. (2017); Raginsky et al. (2017); Brosse et al. (2018); Xu et al. (2018); Cheng and Bartlett (2018); Chatterji et al. <|MaskedSetence|> ... | **A**: (2019a, b); Mou et al.
**B**: (2016); Chen et al.
**C**: (2018); Wibisono (2018); Bernton (2018); Dalalyan and Karagulyan (2019); Baker et al.
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2) The performances of Individual RL and PressLight drop 38% and 41% when the model is transferred. <|MaskedSetence|> MetaLight is more robust to various scenarios than Individual RL and PressLight, and it indicates the advantage of the meta-learning framework. The meta-learning framework could help to learn task-shar... | **A**: That is, given a novel or unseen task, the task-specific information would be represented as latent variable rather than acting as distractors.
**B**: It shows that the models learned by the regular RL algorithms indeed rely on the training scenario.
**C**: Overall, MetaVIM achieves the state-of-the-art perfor... | BCA | ACB | BCA | BCA | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> BestFit works similarly, except that it places the item into the bin of minimum available capacity, which can still fit the item. NextFit has a competitive ratio of 2, while both FirstFit and BestFit are 1.7-competitive (?, ?). Improving upon this performance req... | **A**: FirstFit is another simple heuristic that places an item into the first bin of sufficient space and opens a new bin if required.
**B**:
Online bin packing has a long history of study.
**C**: The simplest algorithm is NextFit, which places an item into its single open bin when possible; otherwise, it closes th... | BCA | BCA | BCA | BAC | Selection 1 |
<|MaskedSetence|> We conduct the autoencoding task for 3D point clouds from three categories in ShapeNet (airplane, car, chair). In this experiment, we compare LoCondA with the current state-of-the-art AtlasNet (Groueix et al., 2018) where the prior shape is either a sphere or a set of patches. Furthermore, we also co... | **A**: The upper bound is produced by computing the error between two different point clouds with the same number of points sampled from the same ground truth meshes.
**B**: We follow the experiment set-up in PointFlow and report performance in both CD and EMD in Table 2.
**C**: In this section, we evaluate how well ... | CBA | ABC | CBA | CBA | Selection 4 |
Paper organization. This paper is organized as follows. Section 2 presents a saddle point problem of interest along with its decentralized reformulation. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> | **A**: In Section 3, we provide the main algorithm of the paper to solve such kind of problems.
**B**: Finally in Section 5, we show how the proposed algorithm can be applied to the problem computing Wasserstein barycenters .
.
**C**: In Section 4, we present the lower complexity bounds for saddle point problems with... | ACB | BCA | ACB | ACB | Selection 4 |
The length of a cycle is its number of edges. <|MaskedSetence|> <|MaskedSetence|> In more concrete terms this problem is equivalent to finding the cycle basis with the sparsest cycle matrix. <|MaskedSetence|> The authors show that the MCB problem is different in nature for each class. For example in [10] a remarkab... | **A**: This problem was formulated by Stepanec [7] and Zykov [8] for general graphs and by Hubicka and Syslo [9] in the strictly fundamental class context.
**B**: In [5] a unified perspective of the problem is presented.
**C**: The minimum cycle basis (MCB) problem is the problem of finding a cycle basis such that th... | CAB | ACB | CAB | CAB | Selection 3 |
<|MaskedSetence|> For instance, the tool by Hohman et al. [63] facilitates the visual comparison of feature distributions for a high number of features. <|MaskedSetence|> incorrect predictions), and multiple data versions. Overall, this tool is less related to the context of our work because it focuses on the challen... | **A**: It visually exposes the divergence of distributions in terms of training and testing splits, predictive performance (correct vs.
**B**: Visual support for the task of feature subset selection requires displaying information on different levels of granularity; highly detailed views are not optimal because they d... | BAC | BAC | BAC | CAB | Selection 3 |
MPC accounts for the real behavior of the machine and the axis drive dynamics can be excited to compensate for the contour error to a big extent, even without including friction effects in the model [4, 5]. <|MaskedSetence|> <|MaskedSetence|> Instead of adapting the controller for the worst case scenarios, the predic... | **A**: In MPC, closed-loop performance is pushed to the limits only if the plant under control is accurately modeled, alternatively, the performance degrades due to imposed robustness constraints.
**B**: High-precision trajectories or set points can be generated prior to the actual machining process following various ... | BAC | BAC | ABC | BAC | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> Neuro-symbolic and graph-based systems could be created that focus on learning and grounding predictions on structured concepts, which have shown promising generalization capabilities [68, 44, 34, 24, 60]. Causality is another relevant line of research, where the goal is to uncover... | **A**: Discovery and usage of causal concepts is a promising direction for building robust systems.
**B**: We have pointed to issues with the existing bias mitigation approaches, which alter the loss or use resampling.
**C**: An orthogonal avenue for attacking bias mitigation is to use alternative architectures.
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2D PoG estimation. <|MaskedSetence|> <|MaskedSetence|> The two datasets both provide calibrated screen pose, where we can convert gaze directions to 2D PoG. GazeCapture dataset collects 2D PoG in mobile devices. <|MaskedSetence|> | **A**: We conduct experiment for 2D PoG estimation.
We use MPIIGaze [49], EyeDiap [185] and GazeCapture [42] for evaluation sets and Euclidean distance for evaluation metric.
**B**: We count the result based on the types of devices, e.g., tablets and phones..
**C**: MPIIGaze and EyeDiap datasets collect 2D PoG in scr... | ACB | CBA | ACB | ACB | Selection 1 |
<|MaskedSetence|> It ensures a lightweight representation that makes the real-world masked face recognition process a feasible task. Moreover, the masked regions vary from one face to another, which leads to informative images of different sizes. The proposed deep quantization allows classifying images from different ... | **A**: This deep quantization technique presents many advantages.
**B**: It instead improves the generalization of the face recognition process in the presence of the mask during the pandemic of coronavirus.
.
**C**: It is worth stating that our proposed method doesn’t need to be trained on the mission region after r... | ACB | ACB | BCA | ACB | Selection 4 |
Sized types are a type-oriented formulation of size-change termination [LJBA01] for rewrite systems [TG03, BR09]. Sized (co)inductive types [BFG+04, Bla04, Abe08, AP16] gave way to sized mixed inductive-coinductive types [Abe12, AP16]. <|MaskedSetence|> We present, to our knowledge, the first sized type system for a c... | **A**: In parallel, linear size arithmetic for sized inductive types [CK01, Xi01, BR06] was generalized to support coinductive types as well [Sac14].
**B**: As we mentioned in the introduction, we use unbounded quantification [Vez15] in lieu of transfinite sizes to represent (co)data of arbitrary height and depth.
**... | ABC | CAB | ABC | ABC | Selection 1 |
The rest of this paper is outlined below. <|MaskedSetence|> <|MaskedSetence|> Subsequently, Section IV introduces the involved fundamental techniques. <|MaskedSetence|> The performance of the two schemes regarding the three problems is evaluated in Section VI followed by the efficiency analysis in Section VII. The ... | **A**: Section III describes the system model, threat model, and design goals.
**B**: The two schemes are constructed in Section V.
**C**: The next section reviews the related work.
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<|MaskedSetence|> The performance improvement of GraphFM compared with the three classes of methods (A, B, C) is especially significant, above 0.010.01\mathbf{0.01}bold_0.01-level. The aggregation-based methods including InterHAt, AutoInt, Fi-GNN and our GraphFM consistently outperform the other three classes of model... | **A**: Our proposed GraphFM achieves best performance among all these four classes of methods on three datasets.
**B**: Compared with the strong aggregation-based baselines AutoInt and Fi-GNN, GraphFM still advances the performance by a large margin, especially on MovieLens-1M dataset.
**C**: I suppose this is becaus... | BAC | ABC | ABC | ABC | Selection 2 |
Self-concordant functions have received strong interest in recent years due to the attractive properties that they allow to prove for many statistical estimation settings [Marteau-Ferey et al., 2019, Ostrovskii & Bach, 2021]. The original definition of self-concordance has been expanded and generalized since its incept... | **A**: This was also the case in Ostrovskii & Bach [2021] and Tran-Dinh et al.
**B**: This was fully formalized in Sun & Tran-Dinh [2019], in which the concept of generalized self-concordant functions was introduced, along with key bounds, properties, and variants of Newton methods for the unconstrained setting which ... | CAB | CAB | CAB | ABC | Selection 1 |
Our algorithm executes several methods (invoked within the loop starting at Algorithm 2 of Algorithm 2), and for most of them it makes a fresh pass over the edges. <|MaskedSetence|> Precisely, the routines are: (1) extend structures along active paths (Extend-Active-Paths), (2) check for edge augmentations (Check-for... | **A**: In total, a Pass-Bundle requires 3333 passes..
**B**: The term Pass-Bundle refers to multiple passes during which those routines are executed.
**C**: The Backtrack-Stuck-Structures method backtracks active paths that were not extended, but does not require a fresh pass.
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Setting. <|MaskedSetence|> This is one of the default learning settings. Based on these settings, we build our settings using the intuition of algorithms (for details about tuning and intuition of our Algorithms, see Section 5.2). In order for the comparison of Algorithm 1 and Algorithm 3 to be fair, it is necessary ... | **A**: That is why we need carefully choose T𝑇Titalic_T (the number of inner/local iterations in Algorithm 1) and p𝑝pitalic_p (probability in Algorithm 3).
**B**: For more details how to choose T𝑇Titalic_T and p𝑝pitalic_p and how to tune level of reliance on the global model see Section 5.2..
**C**: To train ResN... | CAB | CAB | CAB | ABC | Selection 1 |
<|MaskedSetence|> (2017)) is suitable in two-player, constant-sum games. <|MaskedSetence|> We propose to measure convergence to (C)CE ((C)CE Gap in Section E.4) in the full extensive form game. <|MaskedSetence|> We also measure the expected value obtained by each player, because convergence to an equilibrium does no... | **A**: However, it is not rich enough in cooperative settings.
**B**:
Measuring convergence to NE (NE Gap, Lanctot et al.
**C**: A gap, ΔΔ\Deltaroman_Δ, of zero implies convergence to an equilibrium.
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<|MaskedSetence|> The following lemma gives a useful and intuitive characterization of the quantity that the Bayes stability definition requires be bounded. <|MaskedSetence|> The degree to which a query q𝑞qitalic_q overfits to the dataset is expressed by the correlation between the query and that Bayes factor. <|Ma... | **A**: Simply put, the Bayes factor K(⋅,⋅)𝐾⋅⋅{K}\left(\cdot,\cdot\right)italic_K ( ⋅ , ⋅ ) (defined in the lemma below) represents the amount of information leaked about the dataset during the interaction with an analyst, by moving from the prior distribution over
data elements to the posterior induced by some view v... | ACB | CAB | CAB | CAB | Selection 4 |
<|MaskedSetence|> After presenting preliminaries on graphs and sets in Section 2, we prove the mentioned hardness results in Section 3. We present structural properties of antlers and how they combine in Section 4. <|MaskedSetence|> We also prove that, given a large feedback vertex cut, we can shrink it while preserv... | **A**: We conclude in Section 7.
**B**: In Section 5 we show how color coding can be used to find a large feedback vertex cut, if one exists.
**C**: The remainder of the paper is organized as follows.
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We report the results of Poisson image blending [121], GP-GAN [172], Zhang et al. [198], and MLF [194]. We also report the ground-truth composite image obtained using ground-truth alpha matte for comparison. From Fig. 9, it can be seen that the obtained composite images using predicted alpha mattes are very close to t... | **A**: We observe that Poisson image blending [121] smooths the transition boundary to some extent, but unexpectedly distorts the foreground content by seeping through the foreground.
**B**: GP-GAN [172] and Zhang et al.
**C**: [198] are inspired by Poisson image blending, but use content loss to preserve the origina... | ABC | ABC | ABC | ACB | Selection 2 |
Regrettably, currently available open datasets, such as PeMS [8], METR [9] and NYC Cabs [10] are limited to either traffic speeds or taxi-related data. <|MaskedSetence|> Moreover, individual datasets cannot be easily merged into an all-encompassing dataset due to variations in their temporal ranges. For example, while... | **A**: This limitation underscores the urgent need for a comprehensive and spatio-temporally aligned dataset in urban computing to facilitate more precise algorithms and insightful analyses.
**B**: Consequently, they do not provide inclusive support for studies on a realistic and comprehensive smart city system.
**C*... | BAC | CBA | BAC | BAC | Selection 1 |
In the preceding four sections, we introduced different classes of interval estimators, each having its own characteristics. <|MaskedSetence|> We identify four properties that are important for practical purposes. The first one is the main notion of this paper, namely validity, i.e. whether a model is guaranteed to p... | **A**: In this section, we summarize the main properties for clarity and convenience.
**B**: Since none of the methods are valid for any finite-size data set, we only consider validity in the sense of the Marginal Validity Theorem of Section 3.4.
**C**: The second property, which is becoming more and more important d... | ABC | ABC | ABC | BAC | Selection 3 |
Similar to \textcitesimonettaCNW19, we regard melody extraction as a task that identifies the melody notes in a single-track 101010It is common for MIDI files to consist of multiple tracks. <|MaskedSetence|> <|MaskedSetence|> The goal of this task is to distinguish the melody track from other non-melody tracks presen... | **A**: homophonic or polyphonic music.
Utilising the POP909 dataset \textcitepop909, we can develop a model that classifies each Pitch event into vocal melody, instrumental melody or accompaniment, with classification accuracy (ACC) serving as the evaluation metric.111111We note that there is a task closely related to ... | BAC | BAC | BAC | CBA | Selection 1 |
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,” in Proc. <|MaskedSetence|> Conf. <|MaskedSetence|> <|MaskedSetence|> 2006, pp. 369–376.
. | **A**: 23rd Int.
**B**: Learning (ICML), Pittsburgh, USA, Jun.
**C**: Mach.
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<|MaskedSetence|> The outputs from all three branches in CSFR-ISFR perform better than those in ISFR-CSFR, which also supports our argument in section III-D. <|MaskedSetence|> Thus the cross-branch performance is worse in ISFR-CSFR. Then, with the CSFR module trained in the second stage, the information from other sa... | **A**: From the results, if the ISFR module is trained first, the network can overfit within samples and not generalize across samples.
**B**: Therefore the performance from the basic branch is even lower than individual training with the two modules shown in Table I..
**C**:
We also compare the results between the ... | CAB | CAB | CAB | BAC | Selection 3 |
<|MaskedSetence|> The KITTI dataset [11] provides widely used benchmarks for various visual tasks in the autonomous driving, including 2D Object detection, Average Orientation Similarity (AOS), Bird’s Eye View (BEV), and 3D Object Detection. The official data set contains 7481 training and 7518 test images with 2D and... | **A**: This results in a more fair comparison of the results.
**B**: We report our results on the official settings of IoU ≥0.7absent0.7\geq 0.7≥ 0.7 for cars.
.
**C**: Setup.
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<|MaskedSetence|> <|MaskedSetence|> It contains 1,000 training and 500 testing images.
MSRA-TD500 [45] is dedicated to detecting multi-oriented long non-Latin texts. It contains 300 training images and 200 testing images with word-level annotation. <|MaskedSetence|> | **A**: ICDAR2015 [44] includes multi-orientated and small-scale text instances.
**B**: Here, we follow the previous methods [35, 8] and add 400 training images from TR400 [46] to extend this dataset..
**C**: Its ground truth is annotated with word-level quadrangles.
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We formally present a storage strategy for IP addresses that consists of two layers that consist of a limited number of memory blocks. The first layer contains 256×256256256256\times 256256 × 256 memory blocks. <|MaskedSetence|> We allocate a memory block in the other layer for the IP address when its first three par... | **A**: Each element of a memory block in this layer stores the number of occurrences of the corresponding IP address.
**B**: Figure 2 shows an example of the relationship mapping between the memory blocks of two layers and an IP addresses.
**C**: The first three parts of the IP address can be mapped into the correspo... | CAB | ABC | CAB | CAB | Selection 1 |
<|MaskedSetence|> In section 2, we briefly recall the classic saddle point problem and its Schur complement, and introduce the twofold saddle point problem and the form of Schur complement, we then construct and analyze the block-triangular and block-diagonal preconditioners based on Schur complement for twofold saddl... | **A**: Finally, concluding remarks are given in Section 7.
.
**B**: The outline of the remainder of this paper is as follows.
**C**: Some additive Schur complement based preconditioners are constructed and the corresponding known results in the literature are recalled in Section 4 for twofold saddle point problems.
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<|MaskedSetence|> <|MaskedSetence|> The client with the label information calculates the loss and the partial derivatives, which can then be propagated back to the other clients for use in the local gradient steps. This modification would significantly increase the communication cost of the algorithm. <|MaskedSetenc... | **A**: However, in cases when the labels are sensitive and sharing the labels for a sample ID across silos is not feasible, the label information for a sample ID may only be present in a client in one silo.
**B**: In this case, we could modify our algorithm in the following way, similar to (Liu et al., 2020a): the cli... | BAC | ABC | ABC | ABC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> Extensive studies from diverse viewpoints can be found in the references braman2010thirdorder ; Kilmer2013SIAM ; Jin2020 ; Miao2020T ; zheng2020t . Before proceeding with our main results, it is essential to revisit the fundamental concept of T-eigenvalues and T-eigenvectors. Subse... | **A**: They offer a novel perspective to characterize the properties of the widely employed tensor-tensor multiplication (3).
**B**:
Within the framework of tensor-tensor multiplication (3) proposed and investigated by Kilmer and Martin Kilmer2011 , T-eigenvalues and T-eigenvectors have garnered significant attention... | BAC | BAC | BAC | ABC | Selection 3 |
<|MaskedSetence|> The discriminator is shown in Figure 2 (b). The texture branch includes three convolution layers with the kernel size of 4 and stride of 2, tailed by two convolution layers with the kernel size of 4 and stride of 1. We use the Sigmoid non-linear activation function at the last layer and the Leaky ReL... | **A**: Motivated by global and local GANs [7], Gated Convolution [36] and Markovian GANs [9], we develop a two-stream discriminator to distinguish genuine images from the generated ones by estimating the feature statistics of both texture and structure.
**B**: The structure branch shares the same pattern as the upper ... | ABC | ABC | ABC | BCA | Selection 3 |
In this section, we empirically demonstrate the efficiency of MCTS-kSubS and BF-kSubS. <|MaskedSetence|> As a testing ground, we consider three challenging domains: Sokoban, Rubik’s Cube, and INT. All of them require non-trivial reasoning. The Rubik’s Cube is a well-known 3-D combination puzzle. <|MaskedSetence|> <... | **A**: Sokoban is a complex video puzzle game known to be NP-hard and thus challenging for planning methods.
**B**: In particular, we show that they vastly outperform their standard (“non-subgoal”) counterparts.
**C**: INT [54] is a recent theorem proving benchmark..
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In practice, it is extremely hard for those pre-trained language models to tackle this problem. <|MaskedSetence|> However, most character substitution cases exist in glyph and phonetic domains. At the same time, social media hot topics are changing rapidly, creating new expressions or substitutions for original words... | **A**: Models that only saw the original collocations before will naturally suffer from Out-of-Vocabulary (OOV) problems.
**B**: .
**C**: Currently, the tasks for pre-training Chinese language models are mainly focused on the semantic domain, neglecting glyph and phonetic features.
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<|MaskedSetence|> <|MaskedSetence|> A total of 4,560 samples are collected by a template-based method. The language modeling task is to predict the pronoun of a sentence. For NLI and coreference resolution, three variations of each sentence are used to construct entailment pairs. <|MaskedSetence|> | **A**: ABC (Gonzalez et al., 2020), the Anti-reflexive Bias Challenge, is a multi-task benchmark dataset designed for evaluating gender assumptions in NLP models.
**B**: For machine translation, sentences with two variations of third-person pronouns in English are used as source sentences.
.
**C**: ABC consists of 4 ... | ACB | ACB | CBA | ACB | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> They will help to give the authors an approximation of the number of pages that will be in the final version. The structure of the LaTeXfiles, as designed, enable easy conversion to XML for the composition systems used by the IEEE’s outsource vendors. The XML files are used to prod... | **A**: The templates are intended to approximate the final look and page length of the articles/papers.
**B**: Therefore, they are NOT intended to be the final produced work that is displayed in print or on IEEEXplore®.
**C**: Have you looked at your article/paper in the HTML version?
.
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After the main parts of the experiment, subjects were asked a series of survey questions to elicit measures of their individual characteristics. All of these questions were aimed at eliciting a subject’s heterogeneous preferences toward trust and reciprocity. <|MaskedSetence|> In order to distill answers from these q... | **A**: From this, we can see that the first two components of the reciprocity questionnaire describe more than 70%percent7070\%70 % of the variation in survey responses.
**B**: The full set of survey questions is reproduced in Appendix LABEL:app:questions.
**C**: The first principal component, referred to as overall ... | CBA | BAC | BAC | BAC | Selection 2 |
<|MaskedSetence|> Therefore, researchers often apply degradation patterns on the aforementioned datasets to obtain corresponding degraded images to construct paired datasets. However, images in the real world are easily disturbed by various factors (e.g., sensor noise, motion blur, and compression artifacts), resultin... | **A**: To obtain LR images under DN mode, the Bicubic downsampling is performed on the HR image with a scaling factor of 3, and then the Gaussian noise with a noise level of 30 is added to the image..
**B**: For BD, the HR images are blurred by a Gaussian kernel of size 7×7777\times 77 × 7 with standard deviation 1.6 ... | CBA | ACB | CBA | CBA | Selection 3 |
<|MaskedSetence|> This can be done by matching the patch distribution across scales [8, 25, 26, 29]. For blind super-resolution, Neural Knitwork core module is utilized with adjusted losses as illustrated in Figure 5. <|MaskedSetence|> <|MaskedSetence|> This alone could yield an output image resembling the low-resol... | **A**: The queried coordinates for a patch network include all super-resolved coordinates, which means that it is not possible to compute the patch reconstruction loss in this mode.
**B**: However, it is possible to compute the cross-patch consistency loss as well as discriminate the patches to match the source image... | CAB | CAB | CAB | CAB | Selection 1 |
The present paper is the first work we aware of that specifically applies TS to apple tasting, but previous work has considered its use for logistic bandits. For logistic contextual bandits, the implementation of exact TS (i.e. the policy that draws its sample from the exact posterior) is infeasible due to the intract... | **A**: It is therefore necessary to sample from an approximation of the posterior to implement a TS-like approach.
**B**: Dumitrascu et al., (2018) recently proposed an approximation based on Polya-Gamma augmentation (Polson et al.,, 2013; Windle et al.,, 2014) which has improved convergence properties over Laplace ap... | ABC | ABC | ABC | ABC | Selection 1 |
Table 5 reports our results on claim detection. Like we did with unfair clause detection, we evaluate knowledge-agnostic neural baselines and MANN models. First of all, we note how MANNs achieved significant improvements over CNNs and LSTMs, suggesting that the introduced knowledge is indeed beneficial to the task itse... | **A**: We think this should be ascribed to the sparsity of relations between memories and examples in this dataset (see Section 4.2).
**B**: These results support our intuition that controlled, and smart knowledge sampling can be particularly beneficial when limited training data is available.
We observed that with Me... | CBA | CBA | CBA | CBA | Selection 2 |
However, the progress of sentiment dependency-based methods, such as the work by Zhang et al. (2019); Zhou et al. <|MaskedSetence|> (2021); Li et al. <|MaskedSetence|> <|MaskedSetence|> | **A**: (2021), has contributed to the improvement of coherent sentiment learning.
These studies explored the effectiveness of syntax information in ABSC, which mitigates issues related to sentiment coherency extraction..
**B**: (2020); Tian et al.
**C**: (2021a); Dai et al.
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We use IBMQ quantum computers via Qiskit (IBM, [n. d.]) APIs. <|MaskedSetence|> <|MaskedSetence|> The optimization level is set to 2 for all experiments. All experiments run 8192 shots. <|MaskedSetence|> | **A**: The noise models we used are off-the-shelf ones updated by IBMQ team..
**B**: We also employ Qiskit for compilation.
**C**: We study 6 devices, with #qubits from 5 to 15 and Quantum Volume from 8 to 32.
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However, in the current event-based studies, most methods usually handle the fundamental event-based data association problem in implicit ways, which are designed for their specific tasks. <|MaskedSetence|> There are relatively few works focusing on this problem. <|MaskedSetence|> Also, gallego2018unifying ; gallego... | **A**: Due to these difficulties, the aforementioned methods use either indirect or implicit strategies to handle the event-based data association problem.
**B**: As a result, event-based data association has not been effectively solved by the current event-based works.
**C**: As one of the pioneer works in this fiel... | BAC | BCA | BCA | BCA | Selection 2 |
TABLE IV: Unsupervised knowledge distillation. Top-1 accuracy (%) under linear evaluation on STL-10. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> KD denotes the knowledge distillation loss. H+AW denotes the Huber loss and angle-preserving loss in RKD.. | **A**: The teacher model is ResNet-50 pre-trained by MoCo.v2.
**B**: ††{\dagger}† indicates using a momentum encoder as MoCo.v2.
**C**: SSL denotes the InfoNCE loss.
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We used evolutionary search to find the best sub-network architecture under certain constraints. <|MaskedSetence|> <|MaskedSetence|> For each iteration, we only keep the top-20 candidates with the highest accuracy. <|MaskedSetence|> The mutation rate is 0.1. We repeat the process for 30 iterations and choose the sub... | **A**: We randomly sample 100 sub-networks satisfying the constraints to form the first generation of population.
**B**: We use a population size of 100.
**C**: Then we perform crossover to generate 50 new candidates and mutation to generate another 50, forming a new generation.
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<|MaskedSetence|> Unlike the conventional practice of constructing augmented graphs by hand, CGCL employs multiple GNN-based encoders to generate multiple contrastive views. <|MaskedSetence|> <|MaskedSetence|> We then propose the concepts of asymmetric structure and complementary encoders as the design principle for... | **A**: Graph encoders of CGCL learn the graph representations collaboratively, and enhance each other’s learning ability in an unsupervised manner.
**B**: In this study, we introduce CGCL, a novel collaborative graph contrastive learning framework, designed to address the invariance challenge encountered in current GC... | BCA | BCA | CAB | BCA | Selection 2 |
The topic of communication is actively studied in multi-agent RL, see Hernandez-Leal et al., (2020, Table 2) for a recent survey. <|MaskedSetence|> Recent research has shown that strong inductive biases or grounding of communication protocols are necessary for the protocol to be compositional
(see e.g. <|MaskedSetenc... | **A**: For instance,.
**B**: Compositionality is often investigated in the context of signaling games (Fudenberg and Tirole, (1991), Lewis, (1969), Skyrms, (2010), Lazaridou et al., (2018)).
**C**: Kottur et al., (2017), Słowik et al., 2020b ).
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Learning with CBFs: Approaches that use CBFs during learning typically assume that a valid CBF is already given, while we focus on constructing CBFs so that our approach can be viewed as complementary. In [19], it is shown how safe and optimal reward functions can be obtained, and how these are related to CBFs. The aut... | **A**: The authors in [21] consider that uncertainty enters the system dynamics linearly and propose to use robust adaptive CBFs, as originally presented in [22], in conjunction with online set membership identification methods.
**B**: A similar idea is followed in [25] where instead a projection with respect to the C... | ABC | ABC | CBA | ABC | Selection 2 |
In CoauthorshipsNet, node means scientist and weights mean coauthorship, where weights are assigned by the original papers. <|MaskedSetence|> The CoauthorshipsNet has 1589 nodes, however its adjacency matrix is disconnected. Among the 1589 nodes, there are totally 396 disconnected components, and only 379 nodes fall i... | **A**: For this network, there is no ground truth about nodes labels, and the numbers of communities are unknown.
**B**: For convenience, we use CoauthorshipsNet1589 to denote the original network, and CoauthorshipsNet379 to denote the giant component.
**C**: Note that since the overall embeddedness is defined for ad... | ABC | ABC | ABC | BCA | Selection 3 |
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