text_with_holes stringlengths 91 3.85k | text_candidates stringlengths 46 1.47k | A stringclasses 6
values | B stringclasses 6
values | C stringclasses 6
values | D stringclasses 6
values | label stringclasses 4
values |
|---|---|---|---|---|---|---|
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|> Also, our scheme is defined by a sequence of elliptic problems, avoiding the annoyance of saddle point ... | **A**: However, several simplifications are possible when the coefficients have low-contrast, leading to sharper estimates.
**B**: We had to reconsider the proofs, in our view simplifying some of them.
.
**C**: First we consider that T~~𝑇\tilde{T}over~ start_ARG italic_T end_ARG can be nonzero.
| ACB | ACB | ABC | ACB | Selection 2 |
CrowdWisdom: Similar to [18], 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. <|MaskedSetence|> In contrast to mere sentiment features, this approach is more tailored rumor context (... | **A**: In our experiments, “debunking words” is an high-impact feature, but it needs substantial time to “warm up”; that is explainable as the crowd is typically sparse at early stage.
.
**B**: For this purpose, [18] use an extensive list of bipolar sentiments with a set of combinational rules.
**C**: We simplified ... | CBA | BCA | BCA | BCA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> We trade-off this by debunking at single tweet level and let each tweet vote for the credibility of its event. We show the CreditScore measured over time in Figure 13(a). <|MaskedSetence|> In addition, we show the feature analysis for ContainNews (percentage of URLs containing new... | **A**:
We observe that at certain points in time, the volume of rumor-related tweets (for sub-events) in the event stream surges.
**B**: This can lead to false positives for techniques that model events as the aggregation of all tweet contents; that is undesired at critical moments.
**C**: It can be seen that althou... | BCA | ABC | ABC | ABC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> We adapted the L2R RankSVM [12]. The goal of RankSVM is learning a linear model that minimizes the number of discordant pairs in the training data. We modified the objective function of RankSVM following our global loss function, which takes into account the temporal feature specif... | **A**: Multi-Criteria Learning.
**B**: Our task is to minimize the global relevance loss function, which evaluates the overall training error, instead of assuming the independent loss function, that does not consider the correlation and overlap between models.
**C**: The temporal and type-dependent ranking model is l... | ABC | ABC | ABC | BCA | Selection 1 |
<|MaskedSetence|> 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|> In terms of time since being diagnosed with diabetes, patients va... | **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**:
Table 1 shows basic patient information.
| CAB | BCA | CAB | CAB | Selection 4 |
<|MaskedSetence|> A baseline architecture without the ASPP module was constructed by replacing the five parallel convolutional layers with a single 3×3333\times 33 × 3 convolutional operation that resulted in 1,280 activation maps. This representation was then forwarded to a 1×1111\times 11 × 1 convolutional layer wit... | **A**: To quantify the contribution of multi-scale contextual information to the overall performance, we conducted a model ablation analysis.
**B**: While the total number of feature maps stayed constant, the amount of trainable parameters increased in this ablation setting.
**C**: Table 6 summarizes the results acco... | ABC | ABC | ABC | CAB | Selection 2 |
Our strongest positive result about the approximation of the locality number will be derived from the reduction mentioned above (see Section 5.2). However, we shall first investigate in Section 5.1 the approximation performance of several obvious greedy strategies to compute the locality number (with “greedy strategie... | **A**: Secondly, due to the results of Section 4, the investigated greedy strategies for computing the locality number can also be interpreted as greedy strategies for computing the cutwidth of a graph.
**B**: This may provide a new angle to approximating the cutwidth of a graph, i.e., some greedy strategies may only ... | ABC | CAB | CAB | CAB | Selection 3 |
Notable exceptions are the works of
Oh et al. <|MaskedSetence|> (2019), Ha & Schmidhuber (2018), Holland et al. <|MaskedSetence|> <|MaskedSetence|> (2018). Oh et al. (2017) use a model of rewards to augment model-free learning with good results on a number of Atari games. However, this method does not actually aim t... | **A**: (2018) and Azizzadenesheli et al.
**B**: (2017), Sodhani et al.
**C**: (2018), Leibfried et al.
| CAB | BCA | BCA | BCA | Selection 2 |
The whole-body climbing gait involves utilizing the entire body movement of the robot, swaying forwards and backwards to enlarge the stability margins before initiating gradual leg movement to overcome a step. <|MaskedSetence|> To complement this, the rear-body climbing gait was developed. In this approach, once the ... | **A**: This technique optimizes stability during the climbing process.
**B**: For a more detailed discussion of the whole-body climbing gait and the rear-body climbing gait, we direct readers to [10]..
**C**: This strategy is particularly beneficial in situations where the mobility of rolling locomotion is hindered b... | ACB | CBA | ACB | ACB | Selection 1 |
The algorithm classifies items according to their size. Tiny items have their size in the range (0,1/3]013(0,1/3]( 0 , 1 / 3 ], small items in (1/3,1/2]1312(1/3,1/2]( 1 / 3 , 1 / 2 ], critical items in (1/2,2/3]1223(1/2,2/3]( 1 / 2 , 2 / 3 ], and large items in (2/3,1]231(2/3,1]( 2 / 3 , 1 ]. In addition, the algorithm... | **A**: Each critical item is placed in one of the critical bins.
**B**: Large items are placed alone in large bins, which are opened at each arrival.
**C**: Critical bins contain a single critical item, and tiny items up to a total size of 1/3131/31 / 3 per bin, while tiny bins contain only tiny items.
| BCA | BCA | BCA | CAB | Selection 1 |
<|MaskedSetence|> For classifiers supporting incremental classification, like SS3 or MNB, only a small vector needs to be stored for each user. <|MaskedSetence|> of every user and then simply update it as more content is created. However, when working with classifiers not supporting incremental classification, for ev... | **A**:
It is worth noting that the difference in terms of space complexity is also very significant.
**B**: For instance, when using SS3 we only need to store the confidence vector303030In case of ADD, a 2-dimensional vector.
**C**: Note that storing either all the documents or a d×t𝑑𝑡d\times titalic_d × italic_t ... | ABC | ABC | ACB | ABC | Selection 1 |
To investigate UAV networks, novel network models should jointly consider power control and altitude for practicability. Energy consumption, SNR and coverage size are key points to decide the performance of a UAV network [6]. Respectively, power control determines the signal to energy consumption and noise ratio (SNR) ... | **A**: It is because the higher altitude a UAV is, the more users it can support, and the higher SNR it requires.
**B**: Even though UAV systems in 3D scenario with multi-factors of coverage and charging strategies have been studied by [7], it overlooks power control which means that UAVs might wast lots of energy.
*... | ACB | CBA | ACB | ACB | Selection 1 |
<|MaskedSetence|> In Approximation Gradient Error, the error in gradient direction estimation of the cost function leads to inaccurate and extremely different predictions on the learning trajectory through different episodes because of the unseen state transitions and the finite size of experience reply buffer. This t... | **A**:
The sources of DQN variance are Approximation Gradient Error(AGE)[23] and Target Approximation Error(TAE)[24].
**B**: Many of the proposed extensions focus on minimizing the variance that comes from AGE by finding methods to optimize the learning trajectory or from TAE by using methods like averaging to exact ... | ACB | ACB | ACB | BCA | Selection 2 |
<|MaskedSetence|> (2017) used a conditional GAN to generate cardiac MR images from CT images. They showed that utilizing the synthetic data increased the segmentation accuracy and that using only the synthetic data led to only a marginal decrease in the segmentation accuracy. Similarly, Zhang et al. <|MaskedSetence|>... | **A**: (2018c) proposed a GAN based volume-to-volume translation for generating MR volumes from corresponding CT volumes and vice versa.
**B**: (2018b) trained a GAN for translating between digitally reconstructed radiographs and X-ray images and achieved similar accuracy as supervised training in multi-organ segmenta... | CAB | CAB | CAB | BAC | Selection 3 |
Following Fernández-Delgado et al. (2014), each dataset is split into a training and a test set using a 50/50 split while maintaining the label distribution. Afterward, the number of training examples is limited to nlimitsubscript𝑛limitn_{\text{limit}}italic_n start_POSTSUBSCRIPT limit end_POSTSUBSCRIPT examples per c... | **A**: This ensures that the training and validation data are not mixed with the test data.
**B**: We evaluate the training with 5555, 10101010, 20202020, and 50505050 examples per class.
In contrast to Fernández-Delgado et al.
**C**: (2014), we extract validation sets from the training set (e.g., for hyperparameter ... | BCA | BCA | BCA | BAC | Selection 1 |
Broadly speaking, our work is related to a vast body of work on value-based reinforcement learning in tabular (Jaksch et al., 2010; Osband et al., 2014; Osband and Van Roy, 2016; Azar et al., 2017; Dann et al., 2017; Strehl et al., 2006; Jin et al., 2018) and linear settings (Yang and Wang, 2019b, a; Jin et al., 2019;... | **A**: It can be shown that the two settings are incomparable in the sense that one does not imply the other (Zhou et al., 2020).
**B**: We remark that our setting differs from the linear setting studied by Yang and Wang (2019b); Jin et al.
**C**: Despite the differences between policy-based and value-based reinforce... | BAC | BAC | ABC | BAC | Selection 1 |
<|MaskedSetence|> Henry Adams and Dr. Johnathan Bush for very useful feedback about a previous version of this article. We also thank Prof. <|MaskedSetence|> Michael Lesnick for explaining to us some aspects of their work. We thank Dr. <|MaskedSetence|> Finally, we thank Dr. Alexey Balitsky for pointing out an impre... | **A**: Mikhail Katz and Prof.
**B**: We thank Prof.
**C**: Qingsong Wang for bringing to our attention the paper [76] which was critical for the proof of Theorem 1.
| BAC | BAC | BAC | BAC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> For this, we project low-dimensional points into the line (not high-dimensional ones, as in AxiSketcher), and we compute the dimension contributions in a different way, using Spearman’s rank correlation. In summary, although there is a superficial similarity betw... | **A**:
Most similarly to one of our proposed interactions (the Dimension Correlation, Subsection 4.4), in AxiSketcher [47] (and its prior version InterAxis [48]) the user can draw a polyline in the scatterplot to identify a shape, which results in new non-linear high-dimensional axes to match the user’s intentions.
*... | ACB | ABC | ACB | ACB | Selection 3 |
<|MaskedSetence|> Two approaches can be utilized for creating new solutions. The first one is by combination, or crossover of several solutions (Figure 4.d). The classical GA [98] is the most straightforward example of this type. <|MaskedSetence|> <|MaskedSetence|> | **A**: A classical example of stigmergy for creating solutions is ACO [598], in which new solutions are generated by the trace of pheromones left by different agents on a graph representing the solution space of the problem under analysis.
.
**B**: Solution creation, in which new solutions are not generated by mutatio... | BCA | BCA | ACB | BCA | 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... | CAB | CBA | CBA | CBA | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> The query is sent from a spoofed source IP address belonging to the tested network. <|MaskedSetence|> If a query for the requested record arrives from 1.2.3.7, we mark the network as not enforcing ingress filtering. The process is illustrated in Figure 6, steps (1-4) locate the IP... | **A**: Given a DNS resolver at IP 1.2.3.7, we send a DNS query to 1.2.3.7 port 53 asking for a record in domain under our control.
**B**: We monitor for DNS requests arriving at our Name server.
**C**:
Inferring spoofing.
| CAB | BAC | CAB | CAB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> Context-based learning is then introduced to utilize sequential structure across batches of data. 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, which integrates ... | **A**: The results indicate improvement from two sources: The use of neural networks in place of SVMs, and the use of context, particularly in cases where a substantial number of context sequences are available for training.
**B**: First, their approach is extended to a modern version of feedforward artificial neural ... | CBA | BCA | CBA | CBA | Selection 4 |
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]. While t... | **A**: In fact, the construction to generate these semigroups is quite simple [4, Proposition 4.1] (compare also to 3).
**B**: 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 ... | ACB | ACB | CBA | ACB | Selection 2 |
<|MaskedSetence|> (2018) gave better results than human-based attention maps for SCR, we train all of the SCR variants on the subset containing textual explanation-based cues. <|MaskedSetence|> For the first phase, which strengthens the influential objects, we use a learning rate of 5×10−55superscript1055\times 10^{-... | **A**: Since Wu and Mooney (2019) reported that human-based textual explanations Huk Park et al.
**B**: For the second phase, we use a learning rate of 10−4superscript10410^{-4}10 start_POSTSUPERSCRIPT - 4 end_POSTSUPERSCRIPT and weight of 1000100010001000, which is applied alongside the loss term used in the first ph... | ACB | ABC | ACB | ACB | Selection 3 |
<|MaskedSetence|> Existing research has achieved some success using expert annotated corpora of a few hundred or a few thousand privacy policies Wilson et al. (2016); Zimmeck et al. (2019); Ramanath et al. <|MaskedSetence|> More importantly, annotations in the privacy policy domain are expensive. Privacy policies are... | **A**: In contrast, approaches involving large amounts of unlabeled privacy policies remain relatively unexplored..
**B**: (2014), but issues of accuracy, scalability and generalization remain.
**C**:
Natural language processing (NLP) provides an opportunity to automate the extraction of salient details from privacy... | CBA | CBA | CBA | BAC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> (b) displays the normalized importance color legend. The per-model feature accuracy is depicted in (c), and (d) presents the user’s interaction to disable specific features to be used for all the models (only seven features are shown here). This could also happen... | **A**: The y-axis of the table heatmap depicts the data set’s features, and the x-axis depicts the selected models in the current stored stack.
**B**: Univariate-, permutation-, and accuracy-based feature selection is available as long with any combination of them (a).
**C**:
Figure 4: Our feature selection view tha... | CAB | CAB | ABC | CAB | Selection 2 |
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. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> In Persona and Weibo, the ... | **A**: For a fair comparison, each task on this setting also has 120 and 1200 utterances on average in Persona and Weibo respectively.
**B**: (Table 2).
When tasks are similar to each other, MAML performs comparatively poorly.
**C**: We train and evaluate Transformer-F and MAML on this setting.
| ACB | ACB | BAC | ACB | Selection 1 |
<|MaskedSetence|> The TE-aware codeword selection uses the proposed Algorithm 2 and Algorithm 3. <|MaskedSetence|> To evaluate the performance of the proposed two-step scheme, the exhaustive searching scheme for the optimal layer index is also simulated as a comparison, where the traversal of all codebook layers is e... | **A**: As shown in Fig. 13, the sum SE of the TE-aware codeword selection scheme is better than the minimum-beamwidth codeword selection scheme.
**B**: Serving as a reference, the minimum-beamwidth scheme always select the minimum beamwidth, i.e., the maximum number of antenna elements for an activated subarray.
**C*... | CBA | CBA | CBA | ACB | Selection 2 |
<|MaskedSetence|> See also the previous analysis in the NTK regime (Daniely, 2017; Chizat and Bach, 2018a; Jacot et al., 2018; Li and Liang, 2018; Allen-Zhu et al., 2018a, b; Du et al., 2018a, b; Zou et al., 2018; Arora et al., 2019a, b; Lee et al., 2019; Cao and Gu, 2019; Chen et al., 2019a; Zou and Gu, 2019; Ji and ... | **A**: Instead, our analysis combines a generalized notion of one-point monotonicity (Harker and Pang, 1990) and the first variation formula in the Wasserstein space (Ambrosio et al., 2008), which is of independent interest.
.
**B**: Specifically, the previous mean-field analysis casts SGD as the Wasserstein gradient ... | CBA | CBA | CBA | BCA | Selection 1 |
The computation of depth-wise LSTM is the same as the conventional LSTM except that depth-wise LSTM connects stacked Transformer layers instead of tokens in a token sequence as in conventional LSTMs. The gate mechanisms in the original LSTM are to enhance its ability in capturing long-distance relations and to address ... | **A**: In a sense, the layer-by-layer computations in Transformer encoder and decoder stacks are just such sequences where information from a Transformer layer n−1𝑛1n-1italic_n - 1 is passed on to layer n𝑛nitalic_n.
**B**: LSTMs are able to capture long-distance relationships: they can learn to selectively use the r... | BAC | ABC | BAC | BAC | Selection 3 |
There is a rich history of exploration in the field of distortion rectification. The most common method is based on a specific physical model. <|MaskedSetence|> However, these methods cannot handle images captured by other cameras and thus are restricted to the application scenario. <|MaskedSetence|> To overcome the ... | **A**: [15, 16, 17] utilized a camera to capture several views of a 2D calibration pattern that covered points, corners, or other features, and then computed the distortion parameters of the camera.
**B**: Self-calibration was leveraged for distortion parameter estimation in [18, 19, 20]; however, the authors failed i... | CBA | ABC | ABC | ABC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> First, we develop algorithms for the simpler polynomial-scenarios model. <|MaskedSetence|> Finally, we extrapolate the solution to the original black-box problem. This overall methodology is called Sample Average Approximation (SAA).
. | **A**: Second, we sample a small number of scenarios from the black-box oracle and use our polynomial-scenarios algorithms to (approximately) solve the problems on them.
**B**: As usual in two-stage stochastic problems, this has three steps.
**C**: Our main goal is to develop algorithms for the black-box setting.
| CBA | CBA | CBA | CBA | Selection 2 |
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**: 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 given vector.
**B**: Then, we prove that the mean square upper bound of the coupling term between states, network graphs and noi... | ACB | ACB | ACB | CBA | Selection 1 |
Specifically, there are three main steps in the proposed approach. First, MuCo partitions the tuples into groups and assigns similar records into the same group as far as possible. Second, the random output tables, which control the distribution of random output values within each group, are calculated to make similar ... | **A**: Then, MuCo generates an anonymized table in which the original QI values are replaced by the random values according to the random output tables.
.
**B**: Every entry value denotes the probability that the record carries the column value in the anonymized table.
**C**: In the random output tables, the rows cor... | CBA | CBA | ABC | CBA | Selection 2 |
HTC is known as a competitive method for COCO and OpenImage. By enlarging the RoI size of both box and mask branches to 12 and 32 respectively for all three stages, we gain roughly 4 mAP improvement against the default settings in original paper. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> However, the con... | **A**: Armed with DCN, GC block and SyncBN training, our HTC with Res2NetR101 backbone yields 74.58 mAP on validation set, as shown in Table 1.
**B**: (2019) adopted on the third stage gains another 2 mAP.
**C**: Mask scoring head Huang et al.
| CBA | CBA | CBA | CBA | Selection 3 |
We consider the setting of episodic RL with nonstationary reward and transition functions. To measure the performance of an algorithm, we use the notion of dynamic regret, the performance difference between an algorithm and the set of policies optimal for individual episodes in hindsight. <|MaskedSetence|> <|MaskedSe... | **A**: For nonstationary RL, dynamic regret is a stronger and more appropriate notion of performance measure than static regret, but is also more challenging for algorithm design and analysis.
**B**: To incorporate function approximation, we focus on a subclass of MDPs in which the reward and transition dynamics are l... | ABC | ABC | BAC | ABC | Selection 1 |
<|MaskedSetence|> This set was extracted for further analysis and will be henceforth referred to as ‘SG-75’. <|MaskedSetence|> From SG-75, two more subsets were formed via the branching questions. The first contains 59 responses in which respondents said that they have shared news before (referred to as ‘SharedNews-5... | **A**: 75 of the 104 responses fulfilled the criterion of having respondents who were currently based in Singapore.
**B**: The details on the participant demographics of SG-75 are shown in Table 1.
**C**: While these subsets have smaller samples, the self-reported data of the questions falling within the sections of ... | ABC | ABC | ABC | ABC | Selection 2 |
<|MaskedSetence|> For entities with only a few neighbors, the advantage of leveraging DAN is not significant. <|MaskedSetence|> This upward trend halts until the degree exceeds 20. Overall, DAN exhibits significantly better performance than GCN, GAT, or their combination. <|MaskedSetence|> | **A**: However, as the degree increases, incorporating DAN yields more performance gain.
**B**:
The results on the ZH-EN dataset are depicted in Figure 7.
**C**: The decentralized attention, which considers neighbors as queries, consistently outperforms the centralized GAT across varying entity degrees..
| BAC | BAC | CBA | BAC | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> Thus, the trainable parameters of feature extractor are 0. <|MaskedSetence|> ICM and RFM use the same architecture for dynamics estimation with 2.65M parameters. Disagreement utilizes a dynamics ensemble that contains 26.47M parameters. VDM only requires slightly more parameters (... | **A**: ICM estimates the inverse dynamics for feature extraction with 2.21M parameters.
**B**: VDM, RFM, and Disagreement use a fixed CNN for feature extraction.
**C**: We compare the model complexity of all the methods in Table I.
| CBA | CBA | CBA | CAB | Selection 3 |
The model has two parts. First, we apply a DGM to learn only the disentangled part, C𝐶Citalic_C, of the latent space. We do that by applying any of the above mentioned VAEs111In this exposition we use unspervised trained VAEs as our base models but the framework also works with GAN-based or FLOW-based DGMs, supervise... | **A**: We can view this as a style transfer task and use a technique from [adaIN] to achieve our goal..
**B**: For example, in Figure 1, the model uses β𝛽\betaitalic_β-TCVAE [mig] to retrieve the pose of the model as a latent factor.
**C**: In the reconstruction, the rest of the details are averaged, resulting in a ... | BAC | BCA | BCA | BCA | Selection 4 |
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. <|MaskedSetence|> Each element (or cell) is inputted in the same way as three pin structural computing on the upper and lower surfaces. <|MaskedSetence|> The express... | **A**: 1110, NULL: Transmits light that enters the upper and lower sides.
**B**: I will call it this because it is a basic unit that makes up an organization called a window operator.
**C**: ‘Window operator’ is a cube of 3x3, each containing elements of 0,i,1,-1,2, and 2.
| BAC | CBA | CBA | CBA | Selection 4 |
Excluding the interpolating predictor, stability selection produced the sparsest models in our simulations. However, this led to a reduction in accuracy whenever the correlation within features from the same view was of a similar magnitude as the correlations between features from different views. <|MaskedSetence|> In... | **A**: In both gene expression data sets stability selection also produced the sparsest models, but it also had the worst classification accuracy of all meta-learners.
**B**: One could add additional assumptions (Shah \BBA Samworth, \APACyear2013), which may increase predictive performance, but may also increase FDR. ... | ACB | ACB | ABC | ACB | Selection 1 |
Some readers may wonder what the differences are between DepAD and subspace anomaly detection approaches since both use a subset of variables for anomaly detection. We differentiate them in this subsection. To tackle the problem of anomaly detection in high-dimensional data, subspace anomaly detection methods, like tho... | **A**: Fundamentally, subspace anomaly detection methods make use of proximity in subspaces to detect anomalies; DepAD utilizes value deviation based on variable dependency to detect anomalies.
.
**B**: When evaluating anomalousness in each subspace, the criteria used in the proximity-based algorithms, such as LOF and... | CBA | BCA | BCA | BCA | Selection 3 |
CB-MNL enforces optimism via an optimistic parameter search (e.g. <|MaskedSetence|> [2011]), which is in contrast to the use of an exploration bonus as seen in Faury et al. [2020], Filippi et al. [2010]. <|MaskedSetence|> <|MaskedSetence|> [2010] for a short discussion).. | **A**: In non-linear reward models, both approaches may not follow similar trajectory but may have overlapping analysis styles (see Filippi et al.
**B**: Optimistic parameter search provides a cleaner description of the learning strategy.
**C**: in Abbasi-Yadkori et al.
| CBA | CBA | ACB | CBA | Selection 4 |
Cross-scale graph network. <|MaskedSetence|> Then it pools the aggregated features into a smaller temporal scale. Its architecture is illustrated in Fig. 4. <|MaskedSetence|> layer. <|MaskedSetence|> | **A**: The xGN module contains a temporal branch to aggregate features in a temporal neighborhood, and a graph branch to aggregate features from intra-scale and cross-scale locations.
**B**: The temporal branch contains a Conv1d(3,1)Conv1d31\textrm{Conv1d}(3,1)Conv1d ( 3 , 1 )222For conciseness, we use Conv1d(m,n)Co... | ABC | ABC | ABC | CAB | Selection 1 |
<|MaskedSetence|> These papers use bagging [Bre01] and boosting [CG16, FSA99, KMF∗17] techniques for ranking and identifying the best combination of models in different application scenarios. <|MaskedSetence|> On the one hand, we also enable the user to assess the various models and build his/her own ensemble of mode... | **A**: On the other hand, we support the process of generating new models through genetic algorithms and highlight the necessity for the best and most diverse models in the simplest possible voting ensemble.
**B**:
There are relevant works that involve the human in interpreting, debugging, refining, and comparing ens... | BCA | BCA | BCA | BCA | Selection 3 |
<|MaskedSetence|> In [30, 32], semidefinite programming relaxations are proposed for the multi-shape matching problem. <|MaskedSetence|> In [18], a game-theoretic formulation for establishing multi-matchings is introduced. Due to the use of a sparse modelling approach, the method also has the disadvantage that only f... | **A**: However, due to the employed lifting strategy, which drastically increases the number of variables, these methods are not scalable to large problems and only sparse correspondences are obtained.
**B**: The work [26] presents a self-supervised learning approach for finding surface deformations.
**C**: There are... | CBA | CAB | CAB | CAB | 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. <|MaskedSetence|> Unfortunately, ... | **A**: In a few words, an antipodality graph has as vertex set some subgraph of G𝐺Gitalic_G, and two vertices are connected if the corresponding subgraphs of G𝐺Gitalic_G are antipodal.
**B**: We overcome this problem by visiting the connected components in a smart order.
**C**:
The recognition algorithm RecognizeP... | CAB | CAB | CAB | CAB | Selection 3 |
In this paper, we extend the symmetric Laplacian inverse matrix (SLIM) method (SLIM, ) to mixed membership networks and call this proposed method as mixed-SLIM. <|MaskedSetence|> <|MaskedSetence|> And the SLIM method outperforms state-of-art methods in many real and simulated datasets. <|MaskedSetence|> Numerical re... | **A**: As mentioned in SLIM , the idea of using the symmetric Laplacian inverse matrix to measure the closeness of nodes comes from the first hitting time in a random walk.
**B**: SLIM combined the SLIM with the spectral method based on DCSBM for community detection.
**C**: Therefore, it is worth modifying this meth... | ABC | ABC | ABC | ACB | Selection 1 |
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. (2016); Chen et al. <|MaskedSetence|> <|MaskedSetence|> (2017); Brosse et al. (2018); Xu et al. (2018); Cheng and Bartlett (2018); Chatterji et al. (2018); Wibisono (2018); Bernton (2018); ... | **A**: (2016); Dalalyan (2017); Chen et al.
**B**: (2019); Wibisono (2019) and the references therein.
Among these works,.
**C**: (2017); Raginsky et al.
| ACB | ABC | ACB | ACB | Selection 3 |
Our method is compared with the following two categories of methods: conventional transportation methods and RL methods555Some existing RL based methods, such as AttendLight [42] and SD-MaCAR [3], evaluate their methodS under different experimental settings (e.g., road network or traffic flow), and the source codes are... | **A**: Note that for a fair comparison all the RL methods are learned without any pre-trained parameters and the methods are evaluated under the same settings.
**B**: The action interval is five seconds for each method, and the horizon is 3600 seconds for each episode.
**C**: Therefore, they are not compared..
| CAB | BAC | CAB | CAB | Selection 1 |
Online bin packing has a long history of study. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> NextFit has a competitive ratio of 2, while both FirstFit and BestFit are 1.7-competitive (?, ?). Improving upon this performance requires more sophisticated algorithms, and many have been proposed in the literatur... | **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**: BestFit works similarly, except that it places the item into the bin of minimum available capacity, which can still fit the item.
**C**: The simplest algorithm is NextFit, whi... | CAB | CAB | CAB | CAB | 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. <|MaskedSetence|> We f... | **A**: Furthermore, we also compare with l-GAN (Achlioptas et al., 2018) and PointFlow (Yang et al., 2019).
**B**: In this section, we evaluate how well our model can learn the underlying distribution of points by asking it to autoencode a point cloud.
**C**: It can be observed that LoCondA-HC achieves competitive re... | BAC | BAC | BAC | ACB | Selection 3 |
<|MaskedSetence|> This paper is organized as follows. <|MaskedSetence|> <|MaskedSetence|> In Section 4, we present the lower complexity bounds for saddle point problems without individual variables. Finally in Section 5, we show how the proposed algorithm can be applied to the problem computing Wasserstein barycente... | **A**: Section 2 presents a saddle point problem of interest along with its decentralized reformulation.
**B**: Paper organization.
**C**: In Section 3, we provide the main algorithm of the paper to solve such kind of problems.
| BAC | BAC | BAC | CAB | Selection 2 |
The length of a cycle is its number of edges. The minimum cycle basis (MCB) problem is the problem of finding a cycle basis such that the sum of the lengths (or edge weights) of its cycles is minimum. <|MaskedSetence|> In more concrete terms this problem is equivalent to finding the cycle basis with the sparsest cycl... | **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**: For example in [10] a remarkable reduction is constructed to prove that the MCB problem is NP-hard for the strictly fundamental class, while in [11] a polyn... | BCA | ACB | ACB | ACB | Selection 3 |
A use case present in a visual diagnosis tool revealed that feature generation involving the combination of two features is capable of a slight increase in performance [30]. The authors tested the same mathematical operations as in our system (i.e., addition, subtraction, multiplication, and division), but the generati... | **A**: However, the aforementioned VA tools work with regression problems and only support feature selection..
**B**: For the latter, mutual information is used in our VA system (also used by May et al. [26], for instance).
**C**: For the former, one of the most well-known approaches is Pearson’s correlation coeffici... | ACB | CBA | CBA | CBA | Selection 2 |
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|> In MPC, closed-loop performance is pushed to the limits only if the plant under control is acc... | **A**: High-precision trajectories or set points can be generated prior to the actual machining process following various optimization methods, including MPC, feed-forward PID control strategies, or iterative-learning control [6, 7], where friction or vibration-induced disturbances can be corrected.
**B**: Using Bayes... | ACB | CAB | ACB | ACB | Selection 1 |
<|MaskedSetence|> To study this, we train the explicit methods with multiple explicit variables for Biased MNISTv1 and individual variables that lead to hundreds and thousands of groups for GQA and compare them with the implicit methods. For Biased MNISTv1, we first sort the seven total variables in the descending ord... | **A**: Note that conducting the seventh experiment entails annotating each instance with every possible source of bias.
**B**: In the first experiment, the most exploited variable, distractor shape, is used as the explicit bias.
**C**:
It is unknown how well the methods scale up to multiple sources of biases and lar... | ACB | CBA | CBA | CBA | Selection 3 |
<|MaskedSetence|> Wu et al. collect gaze data using near-eye IR cameras [123]. <|MaskedSetence|> <|MaskedSetence|> Kim et al. collect a large-scale dataset of near-eye IR eye images [149]. They synthesize additional IR eye images that cover large variations in face shape, gaze direction, pupil and iris etc.. Lian et... | **A**: They build a multi-branch network to extract the features of each view and concatenate them to estimate 2222D gaze position on the screen.
**B**: Then, they build an eye model using the detected feature and estimate gaze from the gaze model.
**C**: They use CNN to detect the location of glints, pupil centers a... | ACB | BAC | ACB | ACB | Selection 1 |
<|MaskedSetence|> <|MaskedSetence|> 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 sizes in order to handle this issue. Besides, the Deep BoF approach uses a differentiable quanti... | **A**: It ensures a lightweight representation that makes the real-world masked face recognition process a feasible task.
**B**: It is worth stating that our proposed method doesn’t need to be trained on the mission region after removing the mask.
**C**: This deep quantization technique presents many advantages.
| CAB | CAB | CAB | CAB | Selection 4 |
Sized types are a type-oriented formulation of size-change termination [LJBA01] for rewrite systems [TG03, BR09]. <|MaskedSetence|> In parallel, linear size arithmetic for sized inductive types [CK01, Xi01, BR06] was generalized to support coinductive types as well [Sac14]. We present, to our knowledge, the first size... | **A**: 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.
**B**: Sized (co)inductive types [BFG+04, Bla04, Abe08, AP16] gave way to sized mixed inductive-coinductive types [Abe12, AP16].
**C**: However, the sta... | BAC | BAC | CAB | BAC | Selection 4 |
This paper solves the three problems faced by cloud media sharing and proposes two schemes FairCMS-I and FairCMS-II. <|MaskedSetence|> However, utilizing the single-value alteration method for masking the original media content does not achieve the IND-CPA security. <|MaskedSetence|> Notably, both FairCMS-I and Fair... | **A**: In summary, the two proposed schemes can facilitate the media sharing of owners, while simultaneously ensuring the joint protection of copyright and users’ rights, ultimately promoting the sustainable growth of the media sharing industry..
**B**: Then FairCMS-II offers an enhanced privacy solution by replacing ... | CBA | ACB | CBA | CBA | Selection 1 |
It first proposes to connect all the feature fields, and thus the multi-field features can be treated as a fully-connected graph.
Then it utilizes GGNN Li et al. (2015) to model high-order feature interactions on the feature graph. KD-DAGFM Tian et al. <|MaskedSetence|> Other graph-based work, like GFM Xi et al. (2020... | **A**: (2023) uses knowledge distillation and proposes a lightweight student model, directed acyclic graph FM, to learn arbitrary explicit high-order feature interactions from teacher networks.
**B**: And GCFM Zheng et al.
**C**: (2021) uses the multifilter graph-convolved feature crossing (GCFC) layer to learn the n... | ABC | ABC | CBA | 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]. <|MaskedSetence|> For example, the logistic loss function used in logistic regression is not ... | **A**: 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 make use of this property..
**B**: This was also the case in Ostrovskii & Bach ... | CBA | CBA | ABC | CBA | Selection 4 |
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. The term Pass-Bundle refers to multiple passes during which those routines are executed. <|MaskedSetence|> Each of these routines is performed in a separa... | **A**: In total, a Pass-Bundle requires 3333 passes..
**B**: Precisely, the routines are: (1) extend structures along active paths (Extend-Active-Paths), (2) check for edge augmentations (Check-for-Edge-Augmentation), and (3) include (additional) unmatched edges to each structure (Include-Unmatched-Edges).
**C**: The... | CAB | BCA | BCA | BCA | Selection 3 |
For this case we present Algorithm 2. This algorithm is the Tseng method [44] with a resolvent/proximal operator calculation (4). <|MaskedSetence|> Note that we need to communicate with other devices only when we solve the problem (4) and need to multiply by the matrix W𝑊Witalic_W. <|MaskedSetence|> Hence, the probl... | **A**: Here, as in Algorithm 1, the proximal operator is computed inexactly.
**B**: The problem (4) is divided into two minimization subproblems, by X𝑋Xitalic_X, and by Y𝑌Yitalic_Y.
**C**: The following theorem states the convergence rate of Algorithm 2 with Accelerated Gradient Descent.
.
| ABC | ABC | BCA | ABC | Selection 2 |
In Section 2 we provide background on a) correlated equilibrium (CE), an important generalization of NE, b) coarse correlated equilibrium (CCE) (Moulin & Vial, 1978), a similar solution concept, and c) PSRO, a powerful multi-agent training algorithm. <|MaskedSetence|> <|MaskedSetence|> JPSRO requires the solution of... | **A**: We prove that the resulting algorithm converges to a normal form (C)CE in the extensive form game.
**B**: In Section 5 we propose a novel training algorithm, Joint Policy-Space Response Oracles (JPSRO), to train policies on n-player, general-sum extensive form games.
**C**: In Section 3 we propose novel soluti... | CBA | CBA | BAC | CBA | Selection 1 |
<|MaskedSetence|> (2012); Bassily et al. <|MaskedSetence|> <|MaskedSetence|> This builds on intuition that average-case privacy can be viewed from a Bayesian perspective, by restricting some distance measure between some prior distribution and some posterior distribution induced by the mechanism’s behavior (Dwork et... | **A**: (2011)) proposes relaxed privacy definitions that leverage the natural noise introduced by dataset sampling to achieve more average-case notions of privacy.
**B**: (2013); Bhaskar et al.
**C**: Another line of work (e.g., Gehrke et al.
| ABC | CBA | CBA | CBA | Selection 3 |
The remainder of the paper is organized as follows. 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 cu... | **A**: Our main results are derived in Section 6, where we show how color coding can be used to efficiently find antlers when the size of their 𝖺𝗇𝗍𝗅𝖾𝗋𝖺𝗇𝗍𝗅𝖾𝗋\mathsf{antler}sansserif_antler part is bounded in terms of the size of their 𝗁𝖾𝖺𝖽𝗁𝖾𝖺𝖽\mathsf{head}sansserif_head.
**B**: In Section 5 we show ... | BAC | ACB | BAC | BAC | Selection 4 |
In some previous works [154, 29], object placement is used as data augmentation strategy to facilitate the downstream tasks (e.g., object detection, instance segmentation). <|MaskedSetence|> In particular, the foregrounds are cropped out based on the annotated segmentation masks. After removing the foreground objects,... | **A**: [94] released a large-scale object placement assessment dataset named OPA, which consists of 73,470 composite images and their binary rationality labels.
**B**: OPA dataset is constructed by compositing the foregrounds and backgrounds from COCO dataset [89], followed by manually labelling the rationality of obt... | CAB | CAB | CAB | CAB | Selection 2 |
<|MaskedSetence|> 1(c). Service data pertains to the status of urban service providers (e.g. <|MaskedSetence|> weather). Based on this categorization, we have formulated and tested three types of correlations, as shown in Fig. <|MaskedSetence|> | **A**: Interrelationship:
We have classified the sub-datasets into two categories: service data and context data, as depicted in Fig.
**B**: taxi companies), while context data refers to the urban environment (e.g.
**C**: 1(c), correlations (1) among mobility services, (2) among context, such as urban geography, and ... | ABC | ABC | ABC | BAC | Selection 1 |
The results for the conformalized models are the same as for the those trained on half of the data set, since conformal prediction is a post-hoc method. <|MaskedSetence|> <|MaskedSetence|> They either underestimate the uncertainty or produce overconservative prediction intervals. When comparing between the models tra... | **A**: The models incorporating a probabilistic prior (DE, MVE and GP) come out as the most stable ones w.r.t. data size change.
.
**B**: Therefore, only the fully-trained and conformalized models are shown.
**C**: Here again it is clear that the uncalibrated models do not approximately saturate the validity constrai... | BCA | BCA | BCA | BAC | Selection 2 |
<|MaskedSetence|> Given that the tokens we choose do not contain performance information, it is interesting to see how a machine model would “perform” a piece by deciding these volume changes, a task that is essential in performance generation \parencitewidmer94aaai, jeongKKLN19ismir, jeongKKN19icml or expressive perf... | **A**:
Dynamics is an important element in music, as they are often used by musicians to add excitement and emotion to songs.
**B**: In the realm of MIDI, velocity is a parameter that scales the intensity or volume at which a sound sample is played back, with the value ranging from 0 to 127.
**C**: Default MIDI velo... | ABC | ABC | ABC | ACB | Selection 3 |
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,” in Proc. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Learning (ICML), Pittsburgh, USA, Jun. 2006, pp. 369–376.. | **A**: Conf.
**B**: 23rd Int.
**C**: Mach.
| BAC | BAC | BAC | ABC | Selection 3 |
The existing 3D WSSS methods formulate the problem in different directions. <|MaskedSetence|> However, each 3D sample is projected to 2D in several views and each projected 2D image needs pixel-level labeling. <|MaskedSetence|> [11] proposes to generate pseudo point-level label using 3D class activation map[12] from ... | **A**: [10] utilize dense 2D segmentation labels to supervise the training in 3D by projecting the 3D predictions onto the corresponding 2D labels.
**B**: Thus, this method still requires a large amount of manual labeling.
**C**: Initially, we employ the cutting-edge point cloud segmentation network, KPConv[4], as ou... | ABC | BCA | ABC | ABC | Selection 4 |
Setup. 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 3D bounding... | **A**: We report our results on the official settings of IoU ≥0.7absent0.7\geq 0.7≥ 0.7 for cars.
.
**B**: Each class uses different IoU standards for further evaluations.
**C**: This results in a more fair comparison of the results.
| CBA | CBA | CBA | BAC | Selection 1 |
ICDAR2015 [44] includes multi-orientated and small-scale text instances. <|MaskedSetence|> It contains 1,000 training and 500 testing images.
MSRA-TD500 [45] is dedicated to detecting multi-oriented long non-Latin texts. <|MaskedSetence|> <|MaskedSetence|> | **A**: Its ground truth is annotated with word-level quadrangles.
**B**: It contains 300 training images and 200 testing images with word-level annotation.
**C**: Here, we follow the previous methods [35, 8] and add 400 training images from TR400 [46] to extend this dataset..
| ABC | ABC | ABC | ABC | Selection 3 |
<|MaskedSetence|> We can obtain the frequently occurring IP addresses from the statistics, which can be regarded as a pre-processing step of user behavior analysis in network traffic management. Because of the increasing volume and speed of network traffic, it has become expensive and impractical to handle all IP addr... | **A**: In this paper, we present two efficient algorithms for collecting the statistics of large-scale IP address data.
**B**: By taking full advantage of the successive characteristics of memory addresses and the fixed range of each individual part of an IP address, we design two relationship mapping mechanisms betwe... | ABC | ACB | ABC | ABC | Selection 3 |
The authors would like to thank Mingjian Ding, and Baoxuan Zhu for providing an alternative proof of the Hurwitz stability of polynomials (25). They also thank Jarle Sogn for communicating on Schur complement based preconditioners.
The work of M. Cai is partially supported by the NIH-RCMI grant through 347 U54MD013376,... | **A**: RCJC20200714114556020, JCYJ20170818153840322 and JCYJ20190809150413261, and Guangdong Provincial Key Laboratory of Computational Science and Material Design No.
**B**: Ju is supported in part by the National Key R & D Program of China (2017YFB1001604).
**C**: 11971221 and the Shenzhen Sci-Tech Fund No.
| BAC | BCA | BCA | BCA | Selection 3 |
<|MaskedSetence|> In this case, we could modify our algorithm in the following way, similar to (Liu et al., 2020a): the clients in all silos send the intermediate information for a sample to the client that has the label for the sample. The client with the label information calculates the loss and the partial derivati... | **A**: Hence, the convergence analysis given in Section 4 can be trivially extended to this case.
.
**B**: 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.
**C**: We n... | BCA | BCA | BCA | CAB | Selection 3 |
The pseudospectra of finite-dimensional matrices and their extension to linear operators in Banach space have been extensively investigated and summarized in the classical book by Trefethen and Embree trefethen2005spectra . <|MaskedSetence|> The properties of pseudospectra are also discussed, along with a characteriza... | **A**: In the book, four different definitions of matrix pseudospectra are introduced and shown to be equivalent under certain conditions.
**B**: Additionally, for diagonalizable but not necessarily normal matrices, the corresponding Bauer-Fike theorem is presented, which can be found in (trefethen2005spectra, , Theor... | ABC | ABC | ABC | CAB | Selection 1 |
<|MaskedSetence|> We further perform subjective user study. <|MaskedSetence|> They are invited to choose the most realistic image from those inpainted by the proposed method and the representative state-of-the-art approaches. Specifically, each participant has 15 questions, which are randomly sampled from the Places2... | **A**:
User Study.
**B**: We tally the votes and show the statistics in Table 1.
**C**: 10 volunteers with image processing expertise are involved in this evaluation.
| CBA | ACB | ACB | ACB | Selection 4 |
<|MaskedSetence|> Given the hard nature of reasoning problems, these are natural candidates to provide search heuristics [4]. <|MaskedSetence|> These approaches seek solutions using elementary actions. Others, e.g. [29, 33, 23], utilize variational subgoals generators to deal with long-horizon visual tasks. <|Masked... | **A**: Indeed, such a blend can produce impressive results [43, 44, 36, 1].
**B**:
The deep learning revolution has brought spectacular advancements in pattern recognition techniques and models.
**C**: We show that these ideas can be pushed further to provide algorithms capable of dealing with combinatorial complexi... | BAC | BAC | BCA | BAC | Selection 4 |
The backbone of the NER model used in our work is mainly BiLSTM + CRF. The BiLSTM+CRF model is stable and has been verified in many research projects. Meanwhile, as our method is focused on providing a complementary lightweight module for current named entity recognition models, we select two pre-trained language model... | **A**: To be more specific, BERT-base is used in our work, which has 12 transformer layers in total.
**B**: Meanwhile, the Early Stop is also deployed, allowing 5 epochs of loss not decreasing..
**C**: Adam is used as the optimizer and the learning rate is set to 0.002.
| BAC | ACB | ACB | ACB | Selection 3 |
<|MaskedSetence|> The shared modules learn shared features from multiple tasks. <|MaskedSetence|> On the other hand, task-specific modules learn features that are specific to a certain task. Compared with shared modules, task-specific modules are usually much smaller and thus less likely to suffer from overfitting ca... | **A**: The idea behind the modular MTL architecture is simple: breaking an MTL model into shared modules and task-specific modules.
**B**: The robustness of shared modules and the flexibility of task-specific modules makes modular architectures suitable for learning different tasks efficiently.
.
**C**: Since the sha... | ACB | ACB | CBA | ACB | Selection 4 |
<|MaskedSetence|> Therefore, they are NOT intended to be the final produced work that is displayed in print or on IEEEXplore®. 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 th... | **A**: The templates are intended to approximate the final look and page length of the articles/papers.
**B**: Have you looked at your article/paper in the HTML version?
.
**C**: The XML files are used to produce the final print/IEEEXplore® pdf and then converted to HTML for IEEEXplore®.
| CAB | ACB | ACB | ACB | Selection 4 |
Our model departs from the existing literature on public goods in endogenous networks in a number of ways. <|MaskedSetence|> This is the reverse of the situations studied in the previous literature on public goods and sharing on endogenous networks (e.g. <|MaskedSetence|> <|MaskedSetence|> Also in this voluntary sha... | **A**: The cost of linking in this study’s environment is explicitly tied to the effort or contribution level and can be flexibly specified to represent pure or impure (congestive) externalities.
**B**: Primarily, we model a situation in which individuals choose others with whom they would like to share the externalit... | BCA | BCA | BCA | CBA | Selection 1 |
Due to the particularity of the SISR task, it is difficult to construct a large-scale paired real SR dataset. <|MaskedSetence|> However, images in the real world are easily disturbed by various factors (e.g., sensor noise, motion blur, and compression artifacts), resulting in the captured images being more complex th... | **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**: Among them, BI is the most widely used degraded mode to simulate LR images, which is essentially a bicubic down... | CBA | CBA | CAB | CBA | Selection 2 |
<|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. The queried coordinates for a patch network include all super-resolved coordinates, which means that... | **A**:
To perform super-resolution, a Neural Knitwork has to translate the information contained in the patches of the original scale to a domain of patches of finer scale.
**B**: However, it is possible to compute the cross-patch consistency loss as well as discriminate the patches to match the source image distribu... | ABC | ABC | ABC | CBA | Selection 2 |
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. <|MaskedSetence|> It is therefore necessary to sample from an approximation of the posteri... | **A**: Urteaga and Wiggins,, 2018)..
**B**: Appropriately designed approximate algorithms can be successful however, as shown theoretically (Mazumdar et al.,, 2020) for particular Langevin approximation algorithms, and empirically in a range of settings (e.g.
**C**: the policy that draws its sample from the exact pos... | BAC | CBA | CBA | CBA | Selection 4 |
<|MaskedSetence|> However, the introduced priority-based strategies lack proper conditioning on the input, but rather learn the dataset-level importance of each memory slot. <|MaskedSetence|> In our experiments, this led priority-based strategies to learn a quasi-uniform priority distribution. <|MaskedSetence|> For ... | **A**: This works well in unfairness detection, where there are few memory slots and they are associated with many samples; not so well in claim detection, where there are many memory slots, each associated with one or very few samples.
**B**:
The introduction of smart sampling strategies allows scaling to larger mem... | BAC | BAC | CBA | BAC | Selection 4 |
However, the progress of sentiment dependency-based methods, such as the work by Zhang et al. (2019); Zhou et al. (2020); Tian et al. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> | **A**: (2021); Li et al.
**B**: (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..
**C**: (2021a); Dai et al.
| ACB | ACB | ABC | ACB | Selection 4 |
<|MaskedSetence|> MNIST-2 classification result is determined by which feature is larger between the two: feature one is the sum of measurement outcomes of qubit 0 and 1; feature 2 is that of qubit 2 and 3. <|MaskedSetence|> The blue dash line is the classification boundary. The circles/stars are samples of digit ‘3’... | **A**: All the baseline points (yellow) huddled together, and all digit ‘3’ samples are misclassified.
**B**: We visualize the two features obtained from experiments on Belem in a 2-D plane as in Figure 8 right.
**C**: Visualization of QNN extracted features.
| BAC | CBA | CBA | CBA | Selection 3 |
In this paper, we propose a novel unifying event data association (EDA) approach to effectively and explicitly handle the essential event data association and event information fusion problem. The proposed EDA performs a model fitting on event data, which can asynchronously associate and fuse the event data over time ... | **A**: Specifically, EDA presents a deterministic strategy to effectively generate spatio-temporal model hypotheses from the fused retinal events.
**B**: Extensive experiments on several challenging datasets demonstrate the effectiveness and superiority of the proposed EDA..
**C**: Furthermore, different from the pre... | CBA | ACB | ACB | ACB | Selection 3 |
We evaluate the KD tasks based on self-supervised learning on STL-10 dataset. <|MaskedSetence|> We choose multiple smaller networks with fewer parameters as the student network: ResNet-18 [70], MobileNet.v2 [86], ShuffleNet.v1 [87]. <|MaskedSetence|> Follow the linear evaluation protocols in Sec. V-B, we compare the ... | **A**: Similar to the pre-training for the teacher network, we add one additional MLP layer on the basis of the student network.
**B**: In this experiment, we adopt MoCo.v2 with ResNet-50 under 1600-epoch pre-training.
**C**: We adopt the BCE loss for GenURL in the KD task.
.
| ABC | BAC | BAC | BAC | Selection 3 |
<|MaskedSetence|> <|MaskedSetence|> A straightforward way is to split the input image into non-overlapping patches (“Non-overlap”) for the first several blocks as done in [12]. <|MaskedSetence|> It achieves lower image classification accuracy and significantly degraded object detection mAP (on Pascal VOC) due to the... | **A**: The comparison on MobileNetV2 [44] is shown in Table 5.
**B**:
We also compare MCUNetV2 with other methods that reduce inference peak memory.
**C**: Such a practice does not incur extra computation, but it breaks the feature propagation between patches and the translational invariance of CNNs.
| BAC | CAB | BAC | BAC | Selection 3 |
Within CGCL, multiple graph encoders observe input graphs to yield contrastive views. Ideally, these encoders should exhibit complementarity to enhance fitting capability. Specifically, an assembly with encoders possessing non-redundant observation angles demonstrates high complementarity. <|MaskedSetence|> This notio... | **A**: With the above intuition, we define the Complementarity Coefficient of a certain CGCL’s assembly as follows:.
**B**: Redundancies in observation angles can be inferred from overlapping encoder parameters.
**C**: For clarification, we refer the training loss upon completion as the stopping loss.
| BCA | ACB | BCA | BCA | Selection 4 |
The work of Piotr Miłoś was supported by the Polish National Science Center grant UMO-2017/26/E/ST6/00622. <|MaskedSetence|> <|MaskedSetence|> PLG/2019/012498. Our experiments were managed using https://neptune.ai. <|MaskedSetence|> | **A**: We would like to thank the Neptune team for providing us access to the team version and technical support..
**B**: The work of Tomasz Korbak was supported by the Leverhulme Doctoral Scholarship.
**C**: We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet ... | BCA | BCA | CAB | BCA | Selection 4 |
<|MaskedSetence|> In [19], it is shown how safe and optimal reward functions can be obtained, and how these are related to CBFs. <|MaskedSetence|> 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 ... | **A**: In [23], it is shown how additive and multiplicative noise can be estimated online using Gaussian process regression for safe CBFs.
**B**: The authors in [20] use CBFs to learn a provably correct neural network safety guard for kinematic bicycle models.
**C**: Learning with CBFs: Approaches that use CBFs durin... | CAB | CBA | CBA | CBA | Selection 3 |
<|MaskedSetence|> We build theoretical framework on consistent estimation for the proposed algorithm under DCDFM. <|MaskedSetence|> <|MaskedSetence|> When DCDFM degenerates to DCSBM, our results also match classical results under DCSBM. Numerical results of both simulated and real-world networks show the advantage o... | **A**:
(b) To fit DCDFM, an efficient spectral clustering algorithm called nDFA is designed.
**B**: Benefited from the distribution-free property of DCDFM, our theoretical results under DCDFM are general.
**C**: Especially, when DCDFM reduces to DFM, our theoretical results are consistent with those under DFM.
| ABC | ABC | BAC | ABC | Selection 1 |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 6