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**A**:
There are several well-known generating sets for classical groups**B**: Another generating set which has become important in algorithms and applications in the last 10-15 years is the Leedham-Green and O’Brien standard generating set in the following called the LGO generating set. These generators are defined f... | ABC | CAB | BCA | ACB | Selection 4 |
**A**: One difficulty that hinders the development of efficient methods is the presence of high-contrast coefficients [MR3800035, MR2684351, MR2753343, MR3704855, MR3225627, MR2861254]**B**: When LOD or VMS methods are considered, high-contrast coefficients might slow down the exponential decay of the solutions, making... | ABC | ACB | BAC | BCA | Selection 1 |
**A**: These coordinates are computed somehow and their true values can differ from their values stored in the computer**B**: Alg-CM uses an involved subroutine (far more complicated than ours given in Algorithm 1) to update the coordinates in each iteration, which accumulates the inaccuracy of coordinates. Even worse,... | CAB | CBA | CBA | BCA | Selection 4 |
**A**:
Given a tweet, our task is to classify whether it is associated with either a news or rumor. Most of the previous work [6, 11] on tweet level only aims to measure the trustfulness based on human judgment (note that even if a tweet is trusted, it could anyway relate to a rumor)**B**: Our task is, to a point, a r... | CBA | CAB | BAC | ABC | Selection 4 |
**A**: Unlike the case of squared loss, the result for exponential loss are independent of initialization and with only mild conditions on the step size. Here again, we see the asymptotic nature of exponential loss on separable data nullifying the initialization effects thereby making the analysis simpler compared to s... | CAB | CBA | CAB | BAC | Selection 2 |
**A**: There is a big bias between the sentiment associated to rumors and the sentiment associated to real events in relevant tweets. In specific, the average polarity score of news event is -0.066 and the average of rumors is -0.1393, showing that rumor-related messages tend to contain more negative sentiments**B**: F... | CBA | CBA | ABC | BAC | Selection 4 |
**A**: The baseline and the best results of our 1stsuperscript1𝑠𝑡1^{st}1 start_POSTSUPERSCRIPT italic_s italic_t end_POSTSUPERSCRIPT stage event-type classification is shown in Table 3-top**B**:
Results**C**: The accuracy for basic majority vote is high for imbalanced classes, yet it is lower at weighted F1. Our le... | CBA | BAC | BCA | CBA | Selection 2 |
**A**: Only one of the patients suffers from diabetes type 2 and all are in ICT therapy. 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.**B**: Body weight, according to BMI, is normal for half of the patien... | CAB | BAC | CBA | ACB | Selection 3 |
**A**: Table 6 summarizes the results according to validation instances of five eye tracking datasets for the model with and without an ASPP module. It can be seen that our multi-scale architecture reached significantly higher performance (one tailed paired t-test) on most metrics and is therefore able to leverage the ... | BCA | CAB | CAB | CBA | Selection 4 |
**A**:
As mentioned several times already, our reductions to and from the problem of computing the locality number also establish the locality number for words as a (somewhat unexpected) link between the graph parameters cutwidth and pathwidth**B**: We shall discuss in more detail in Section 6 the consequences of this... | CBA | ABC | BAC | BAC | Selection 2 |
**A**: In this work we refer to MDPs as environments and assume that environments do not provide direct access to the state (i.e., the RAM of Atari 2600 emulator)**B**: A single image does not determine the state.
In order to reduce environment’s partial observability, we stack four consecutive frames and use it as the... | ACB | CAB | ABC | CAB | Selection 1 |
**A**: Similar to many other hybrid robots, the default locomotion mode of the Cricket robot is rolling. This mode is preferred on flat and rigid surfaces due to its efficiency in terms of time and energy consumption. In the rolling locomotion mode, the robot maintains its home configuration, where all joints are posit... | ACB | ACB | ACB | CBA | Selection 4 |
**A**: Namely, all previous works assume that advice is, in all circumstances, completely trustworthy, and precisely as defined by the algorithm**B**:
In this work, we address what is a significant drawback in the online advice model**C**: Since the advice is infallible, no reasonable online algorithm with advice woul... | CAB | BAC | BCA | ABC | Selection 2 |
**A**: those caused by not using other information than text for classification, another limitation in the present work is that we used words as the basic building blocks (i.e**B**: each writing was processed as a Bag of Words) on which our approach begins to process other higher level blocks (like sentences and paragr... | CAB | BAC | BCA | CAB | Selection 3 |
**A**: Table 2 and Figure 4 show the performance under non-IID data distribution**B**: Furthermore, we can find that the momentum factor masking trick will severely impair the performance of DGC under non-IID data distribution.
**C**: We can find that GMC can achieve much better test accuracy and faster convergence spe... | ACB | BCA | BCA | BAC | Selection 1 |
**A**:
, where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks**B**: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization**C**: operation. | ABC | CBA | CBA | CBA | Selection 1 |
**A**: The UAV might select the largest power to increase utility. However, The more power one UAV uses, the more interference other UAVs will receive and other UAVs’ utilities will reduce. For the sake of enlarging the global utility, the largest power is not the optimal strategies for the whole UAV ad-hoc network. Th... | ABC | CAB | BCA | ACB | Selection 2 |
**A**: For the FEMM model used to find ψmainsubscript𝜓𝑚𝑎𝑖𝑛\psi_{main}italic_ψ start_POSTSUBSCRIPT italic_m italic_a italic_i italic_n end_POSTSUBSCRIPT,
the dc current in the main coil was set to Imain=subscript𝐼𝑚𝑎𝑖𝑛absentI_{main}=italic_I start_POSTSUBSCRIPT italic_m italic_a italic_i italic_n end_POST... | ACB | CAB | BCA | BAC | Selection 3 |
**A**: However, our experiments were limited to simple problems and environments, utilizing small network architectures and only two Dropout methods.
**B**: Our findings indicate that the Dropout-DQN method is effective in decreasing both variance and overestimation**C**: In this study, we proposed and experimentally a... | BAC | CBA | ABC | BAC | Selection 2 |
**A**:
Table 3: A summary of medical image segmentation papers along with their type of proposed improvement**B**: For example, if a paper reports counts of patients, volumes, slices, etc., we report the count of patients.**C**: * indicates the count at the highest level | ACB | ABC | CAB | CBA | Selection 1 |
**A**: The other datasets show similar characteristics**B**: The overall evaluation on all datasets is presented in the next section.
The number of training examples per class is shown in parentheses and increases in each row from left to right.**C**: The results are shown in Figure 3 exemplarily for the Car, Covertype... | CBA | BCA | ABC | ABC | Selection 2 |
**A**:
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.... | ACB | CBA | CBA | CBA | Selection 1 |
**A**: Furthermore, in particular for the TPU, experimentation is often hindered due to limitations in the tool chain which is not flexible enough to support such optimizations**B**: They are not suited to execute generic compressed models and are therefore not included in the following experiments.
**C**: While domain... | ABC | CBA | CBA | BCA | Selection 4 |
**A**: Finally, recently we became aware of [81, Lemma 5.1], which is similar to Theorem 5**B**: The author considers spaces with numerable covers (i.e**C**: the spaces admit locally finite partition of unity subordinate to the covers), whereas in our version that condition is automatically satisfied since we only cons... | CAB | ABC | CAB | ACB | Selection 2 |
**A**: Personally, Anna does not completely trust the decisions made from automatic algorithms (such as classifiers), so she would prefer to use an interactive visualization.**B**:
Anna is a medical student who is enthusiastic about becoming a specialist in identifying and treating breast cancer**C**: She heard about ... | BCA | ABC | BAC | CAB | Selection 4 |
**A**: We believe that this dual criterion can be very useful for researchers**B**: The first one helps classify the different proposals by their origin of inspiration, whereas the second one provides valuable information about their algorithmic similarities and differences**C**: This double classification allows resea... | ABC | ACB | BAC | CBA | Selection 1 |
**A**: To illustrate the process of AdaGAE, Figure 2 shows the learned embedding on USPS at the i𝑖iitalic_i-th epoch**B**: An epoch means a complete training of GAE and an update of the graph**C**: The maximum number of epochs, T𝑇Titalic_T, is set as 10. In other words, the graph is updated 10 times. Clearly, the emb... | BAC | BCA | ABC | BAC | Selection 3 |
**A**: **B**: This is not surprising, since the number of web servers is much larger than the others and it is recommended not to block ICMP to Web servers to allow for path MTU discovery**C**:
As can be seen in Table 3 the most applicable technique is PMTUD against Web servers, which applies to a bit more than 87% of... | BCA | CBA | BCA | ABC | Selection 2 |
**A**:
The context pathway is based on a recurrent neural network (RNN) approach**B**: It reuses weights and biases across the steps of a sequence and can thus process variable-length sequences**C**: The alternative was to use a long-short term memory (LSTM), which employs gating variables to better remember informati... | BAC | ABC | CAB | ACB | Selection 2 |
**A**: The version for automaton semigroups does not follow directly from 8, as the free monogenic semigroup is not a complete automaton semigroup [4, Proposition 4.3] or even a (partial) automaton semigroup (see [8, Theorem 18] or [20, Theorem 1.2.1.4]).
**B**: As an example, we prove in the following theorem that it ... | BCA | CBA | BCA | BAC | Selection 2 |
**A**:
Following Selvaraju et al. (2019), we report Spearman’s rank correlation between network’s sensitivity scores and human-based scores in Table A3. For HINT and our zero-out regularizer, we use human-based attention maps**B**: For SCR, we use textual explanation-based scores. We find that HINT trained on human at... | ABC | BCA | CAB | CAB | Selection 1 |
**A**: PrivBERT will help improve results on various tasks in the privacy domain and help build stable and reliable privacy preserving technology**B**: This should benefit internet users, regulators, and researchers in many ways.**C**:
We believe that the PrivaSeer Corpus will help advance research techniques to autom... | BCA | ACB | BAC | ABC | Selection 1 |
**A**: Above all, mixtures of stacked models have been deployed to increase the performance of results in medicine [39, 30, 37].
In the case of healthcare-related problems, however, the difficulties of stacking lead to an even worse situation, because interpretability, fairness in decisions, and trustworthiness of ML m... | CAB | ABC | CBA | ACB | Selection 3 |
**A**: In Persona we use a pre-trained natural language inference model to measure the response consistency with persona description for C Score. In Weibo, users do not have persona descriptions, so we pre-train a user classifier to classify the generated responses, and use the accuracy for C Score.**B**: We use PPL an... | CBA | BCA | ACB | CAB | Selection 1 |
**A**: Hence, the historical MSI can be used to predict the future MSI. According to the GP-based MSI prediction algorithm, the predicted position and attitude are estimated by the mean of the predictive distribution of the outputs (the future MSI) on the specific test dataset. The predictive distribution of the output... | CAB | BCA | BAC | BAC | Selection 1 |
**A**: Note that the 1111-color case with the completeness requirement is not very interesting, and also not useful for the general case: completeness states that every node on
the left must be connected, via the unique edge relation, to every node on the right – regardless of the matrix**B**: This will be bootstrapped... | BAC | CBA | ABC | CBA | Selection 1 |
**A**: 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,... | CBA | BCA | CAB | ABC | Selection 4 |
**A**: For machine translation, the performance of the Transformer translation model Vaswani et al. (2017) benefits from including residual connections He et al. (2016) in stacked layers and sub-layers Bapna et al. (2018); Wu et al. (2019b); Wei et al. (2020); Zhang et al. (2019); Xu et al**B**: (2020a); Li et al. (202... | ABC | BAC | CBA | CAB | Selection 1 |
**A**: furthermore B→C→𝐵𝐶B\to Citalic_B → italic_C**B**: Then A~⊧φmodels~𝐴𝜑\widetilde{A}\models\varphiover~ start_ARG italic_A end_ARG ⊧ italic_φ because A→A~→𝐴~𝐴A\to\widetilde{A}italic_A → over~ start_ARG italic_A end_ARG and**C**: Apply [33, Corollary
5.14] to A𝐴Aitalic_A and B𝐵Bitalic_B | CBA | ACB | BCA | BAC | Selection 2 |
**A**: Note that we only use 1/4 distorted image to predict the ordinal distortion**B**: (1) Overall, the ordinal distortion estimation significantly outperforms the distortion parameter estimation in both convergence and accuracy, even if the amount of training data is 20% of that used to train the learning model**C**... | CAB | CAB | BAC | CAB | Selection 3 |
**A**: The momentum coefficient is set as 0.9 and the weight decay is set as 0.001**B**: The initial learning rate is selected from {0.001,0.01,0.1}0.0010.010.1\{0.001,0.01,0.1\}{ 0.001 , 0.01 , 0.1 } according to the performance on the validation set**C**: We do not adopt any learning rate decay or warm-up strategies.... | ABC | CAB | BCA | ACB | Selection 1 |
**A**: See Appendix A for an in-depth discussion.**B**:
Unfortunately, standard SAA approaches [26, 7] do not directly apply to radius minimization problems**C**: On a high level, the obstacle is that radius-minimization requires estimating the cost of each approximate solution; counter-intuitively, this may be harder... | CBA | BCA | BCA | CAB | Selection 4 |
**A**: Both the weights of different edges in the network graphs at the same time instant and the network graphs at different time instants may be mutually dependent.) rather than i.i.d**B**: Besides, the network graphs may change randomly with spatial and temporal dependency (i.e**C**: graph sequences as in [12]-[15],... | ACB | BCA | BAC | ABC | Selection 3 |
**A**: The sequence of the query is expressed in the following form
SELECT SUM(salary) FROM Microdata**B**: We randomly generate 1,000 queries and calculate the average relative error rate for comparison**C**: In this experiment, we use the approach of aggregate query answering [37] to check the information utility of ... | ACB | CAB | CAB | CBA | Selection 4 |
**A**: (2019) are used as our backbones, pretrained on ImageNet only. SGD with momentum 0.9 and weight decay 1e-4 is adopted. The initial learning rate is set to 0.01 for Res2Net101 and 0.02 for X101-64x4d defaultly and decayed by factor 0.1 at epoch 32**B**: During training process, the batch size is 8 (one image per ... | BAC | CAB | BAC | BCA | Selection 4 |
**A**: As was written in the previous version, an anonymous referee of version 1 wrote that the theorem was known to experts but not published**B**: Maybe the presentation below is what was known. **C**:
Here we give an embarrassingly simple presentation of an example of such a function (although it can be shown to be... | ACB | BCA | BAC | BAC | Selection 2 |
**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. To incorporate function approximation, we focus on a subclass of MDPs in which the reward and transition dynamics are linear in... | BCA | CAB | ACB | ABC | Selection 1 |
**A**:
In this study, we seek to answer these research questions. RQ1: How much do people trust the media by which they obtain news? RQ2: Why do people share news and how do they do it? RQ3: How do people view the fake news phenomenon and what measures do they take against it? An online survey was employed for data co... | CAB | ACB | CBA | BCA | Selection 2 |
**A**: While some methods attempt to address entity alignment by introducing a new relation, the results often demonstrate poor performance, as evidenced in [2, 27].**B**:
Our method represents a standard KG embedding approach capable of generating embeddings for various tasks**C**: This distinguishes it from most ind... | CAB | BAC | ACB | ABC | Selection 1 |
**A**: The gradient of vanilla policy gradient [16] is defined as
**B**: The goal of RL is to find a policy that maximizes the expected cumulative reward**C**: Policy gradient methods solve RL problem by iteratively following a parameterized policy, sampling data from the parameterized policy, and updating the paramete... | CAB | ABC | CBA | CBA | Selection 1 |
**A**: In doing so, we revisit earlier results by Carl de Boor and Amon Ros [28, 29] and answer their question from our perspective.**B**: We complement the established notion of unisolvent nodes by the dual notion of unisolvence**C**: That is: For given arbitrary nodes P𝑃Pitalic_P, determine the polynomial space ΠΠ\P... | ACB | CBA | BAC | CAB | Selection 4 |
**A**: the disentangled factors) and correlated components Z𝑍Zitalic_Z, a.k.a as nuisance variables, which encode the details information not stored in the independent components. A series of works starting from [beta] aims to achieve that via regularizing the models by up-weighting certain terms in the ELBO formulati... | ABC | BCA | ABC | CAB | Selection 2 |
**A**: We will look at the inputs through 18 test cases to see if the circuit is acceptable**B**: As mentioned above, the search is carried out and the results are expressed by the unique number of each vertex. The result is as shown in Table. 1. The result of moving from the K2 peak to the K1 peak is the same as that ... | BCA | ACB | ABC | ABC | Selection 2 |
**A**: Given a finite subset of such permutations, we can compute a group generated by this set**B**:
A finite field, by definition, is a finite set, and the set of all permutation polynomials over the finite field forms a group under composition**C**: In this paper, we propose a representation of such a group using t... | ABC | BAC | ACB | CBA | Selection 2 |
**A**: 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. In both gene e... | ABC | BAC | BAC | CAB | Selection 1 |
**A**: The violin plot’s outline indicates the Gaussian kernel density estimated from these results, with a red dot for the mean and lines indicating standard deviation. Techniques are pairwise compared using the Wilcoxon rank-sum test, with the left presumed superior; p𝑝pitalic_p-values are displayed above each pair.... | ACB | BCA | BCA | CBA | Selection 4 |
**A**: [2010], Li et al**B**: Next we show how using a global lower bound in form of κ𝜅\kappaitalic_κ (see Assumption 2) early in the analysis in the works Filippi et al**C**: [2017], Oh & Iyengar [2021] lead to loose prediction error upper bound. For this we first introduce a new notation:
| CBA | BAC | ACB | ABC | Selection 2 |
**A**: Table 6: xGN levels in xGPN (ActivityNet-v1.3)**B**: The levels in the columns with ✓use xGN and the ones in the blank columns use a Conv1d(3,2)Conv1d32\textrm{Conv1d}(3,2)Conv1d ( 3 , 2 ) layer instead.
**C**: We show the mAPs (%) at different tIoU thresholds, average mAPs as well as mAPs for short actions (le... | CBA | ACB | ABC | CBA | Selection 2 |
**A**: This entire process should be trackable and manageable from the user’s side. The best models (according to the user) are accumulated in a final bucket, forming a majority-voting ensemble.
**B**: Li et al. [LCW∗18] found that once the ML expert has acquired all the results from an execution stage, he/she should a... | CBA | BCA | BCA | CAB | Selection 4 |
**A**: The decentralized state-dependent Markov matrix synthesis (DSMC) algorithm is introduced in Section III.
Section IV introduces the probabilistic swarm guidance problem formulation, and presents numerical simulations of swarms converging to desired distributions. The paper is concluded in Section V.**B**: Section... | CBA | BCA | CAB | BAC | Selection 1 |
**A**: Although this way there is no bias, in general the resulting correspondences are not cycle-consistent**B**: Alternatively, one could solve pairwise shape matching problems between all pairs of shapes in the shape collection**C**: As such, matching shape A via shape B to shape C, may lead to a different correspon... | BCA | BCA | CBA | BAC | Selection 4 |
**A**: Gavril [8] proves that a graph is chordal if and only if it is the intersection graph of subtrees of a tree. We can recognize chordal graphs in O(m+n)𝑂𝑚𝑛O(m+n)italic_O ( italic_m + italic_n ) time [21, 23].**B**:
Path graphs and directed path graphs are classes of graphs between interval graphs and chordal ... | BAC | CBA | BAC | CAB | Selection 4 |
**A**: Subfigure 1(b) suggests that Mixed-SLIM significantly outperforms Mixed-SCORE, OCCAM, and GeoNMF under the DCMM setting. It is interesting to find that only Mixed-SLIM enjoys better performances as the fraction of pure nodes increases under the DCMM setting.**B**:
Numerical results of these two sub-experiments ... | ACB | ACB | CAB | BAC | Selection 3 |
**A**: (2017); Agarwal et al. (2018); Zhang et al. (2018); Tripuraneni et al**B**: (2018); Boumal et al. (2018); Bécigneul and Ganea (2018); Zhang and Sra (2018); Sato et al. (2019); Zhou et al. (2019); Weber and Sra (2019) and the references therein.
Also see recent reviews (Ferreira et al., 2020; Hosseini and Sra, 20... | CBA | CAB | CAB | BCA | Selection 4 |
**A**: To handle this challenge, we formulate the policy learning in a road network as a meta-learning problem, where traffic signal control at each intersection corresponds to a task, and a policy is learned to adapt to various tasks. Reward function and state transition of these tasks vary but share similarities sinc... | ACB | CAB | CBA | BAC | Selection 3 |
**A**: Concerning the application of frequency predictions in competitive online optimization, we note that, perhaps surprisingly, such predictions have not been used widely, despite their simplicity and effectiveness**B**: Recently, and concurrently with the conference version of our work, (?) studied the online knaps... | ACB | ABC | BCA | CAB | Selection 1 |
**A**: (HC) denotes the HyperCloud autoencoder in LoCondA, and (HF) - the HyperFlow autoencoder. For HyperCloud and HyperFlow, we use both variants of the models that generate point clouds (P) and meshes (M). **B**: Table 1: Generation results**C**: MMD-CD scores are multiplied by
103superscript10310^{3}10 start_POSTSU... | CAB | ABC | CBA | ABC | Selection 1 |
**A**: Paper organization. This paper is organized as follows**B**: Section 2 presents a saddle point problem of interest along with its decentralized reformulation. In Section 3, we provide the main algorithm of the paper to solve such kind of problems**C**: In Section 4, we present the lower complexity bounds for sad... | BAC | BAC | ABC | BAC | Selection 3 |
**A**: In [5] a unified perspective of the problem is presented. The authors show that the MCB problem is different in nature for each class. 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 polynomial time algorithm i... | ABC | CBA | BAC | BCA | Selection 2 |
**A**: One immediate application of Theorem 1.2 is the reduction of fractional Helly numbers**B**: For instance, it easily improves a theorem444[35, Theorem 2.3] was not phrased in terms of (K,b)𝐾𝑏(K,b)( italic_K , italic_b )-free covers but readily generalizes to that setting, see Section 1.4.1**C**: of Patáková [35... | ACB | ACB | BAC | ABC | Selection 4 |
**A**: They may, however, lack transparency without the assistance of visualizations [19, 16, 17, 20, 21]. Furthermore, there is an opportunity to select features from a candidate set, which can be time-consuming if this set is large [22, 23, 24]. Even though a series of analytical tools and systems have been developed... | ABC | ABC | CAB | CBA | Selection 4 |
**A**: We have implemented the simulations in Matlab, using Yalmip/Gurobi to solve the corresponding MPCC quadratic program in a receding horizon fashion and the GPML library for Gaussian process modeling**B**:
We use two geometries to evaluate the performance of the proposed approach, an octagon geometry with edges i... | ACB | CBA | BAC | ABC | Selection 3 |
**A**: Systems can exploit correlated variables even if they are not directly a part of the input e.g., through inferred zip codes [21], failing to work effectively on minority groups.
**B**: While this is a toy problem, in the real world, hidden minority patterns are common and failing on them can have dire consequenc... | CAB | BAC | BCA | ACB | Selection 1 |
**A**: In 2019, Kellnhofe et al. proposed the Gaze360 dataset [43]. The dataset consists of 238 subjects of indoor and outdoor environments with 3D gaze across a wide range of head poses and distances**B**: For example, in 2018, Fischer et al. proposed RT-Gene dataset [54]. This dataset provides accurate 3D gaze data s... | BCA | CBA | ABC | BAC | Selection 4 |
**A**: The proposed method outperformed TL-based method using the same pre-trained models. This performance is explained by the fact that the fc layers of the pre-trained models are more dataset-specific features (generally pre-trained on ImageNet dataset) which is a very different dataset, thus, this strategy is not a... | CBA | CAB | BCA | CAB | Selection 1 |
**A**: It is clear that the program terminates by lexicographic induction on (i,j)𝑖𝑗(i,j)( italic_i , italic_j ).
**B**: If the processor issues a “get,” then the head of the input stream is consumed, recursing on its tail**C**: Otherwise, the output stream is constructed recursively, first issuing the element receiv... | CAB | CBA | BCA | CBA | Selection 1 |
**A**: This is particularly important, as we mentioned in Section IV-B, because only in this way, the increase in the number of users will not overload the cost of the owner, which is a major problem faced in the AFP scheme**B**: Based on the above reasons, we conclude that both FairCMS-I and FairCMS-II can be regarded... | CAB | ACB | BAC | BCA | Selection 2 |
**A**: (2019), and recommender systems Wang et al. (2019); Wu et al. (2019).**B**:
Currently, Graph Neural Networks (GNN) Kipf and Welling (2017); Hamilton et al. (2017); Veličković et al**C**: (2018) have recently emerged as an effective class of models for capturing high-order relationships between nodes in a graph ... | BAC | ACB | BAC | CAB | Selection 4 |
**A**: For example, the logistic loss function used in logistic regression is not strictly self-concordant, but it fits into a class of pseudo-self-concordant functions, which allows one to obtain similar properties and bounds as those obtained for self-concordant functions [Bach, 2010]. This was also the case in Ostro... | BAC | CBA | ABC | ABC | Selection 1 |
**A**:
It is known that finding an exact matching requires linear space in the size of the graph and hence it is not possible to find an exact maximum matching in the semi-streaming model [FKM+04], at least for sufficiently dense graphs**B**: We call an algorithm an α𝛼\alphaitalic_α-approximation if the matching has ... | ABC | CBA | ACB | BAC | Selection 3 |
**A**: We show CPP achieves linear convergence rate under strongly convex and smooth objective functions.
**B**: In this paper, we consider decentralized optimization over general directed networks and propose a novel Compressed Push-Pull method (CPP) that combines Push-Pull/𝒜ℬ𝒜ℬ\mathcal{A}\mathcal{B}caligraphic_A c... | CAB | BCA | ABC | ABC | Selection 1 |
**A**: One can note a branch of recent work devoted to solving non-smooth problems by reformulating them as saddle point problems [8, 9], as well as applying such approaches to image processing
[10, 11]**B**: Recently, significant attention was devoted to saddle problems in machine learning**C**: For example, Generativ... | ACB | BAC | ACB | ABC | Selection 4 |
**A**: This game is difficult because it requires searching over a large number of policies to find a compatible mapping, and can easily fall into a sub-optimal equilibrium**B**:
Trade Comm is a two-player, common-payoff trading game, where players attempt to coordinate on a compatible trade**C**: Figure 2(b) shows a ... | ACB | ABC | CBA | BAC | Selection 4 |
**A**: Part this work was completed while Ligett was visiting Princeton University’s Center for Information Technology Policy.
**B**: This work was supported in part by a gift to the McCourt School of Public Policy and Georgetown University, Simons Foundation Collaboration 733792, Israel Science Foundation (ISF) grant ... | CAB | CBA | CBA | ABC | Selection 1 |
**A**:
However, we argue that these results on kernelization do not explain the often exponential speed-ups (e.g. [3], [5, Table 6]) caused by applying effective preprocessing steps to non-trivial algorithms**B**: Instead, fixed-parameter tractable (FPT) algorithms have a running time that scales polynomially with the... | ACB | CBA | CAB | ABC | Selection 1 |
**A**: Cun and Pun [22] considered the problem of blind image harmonization. They proposed to predict the inharmonious region mask in the attention block, which deals with the foreground and background separately according to the predicted mask. Subsequently, some works [84, 85, 174, 173] focus on inharmonious region l... | ACB | ACB | BCA | CAB | Selection 3 |
**A**: At every timestamp τ𝜏\tauitalic_τ, we use this policy to dispatch available taxis to current passengers, with the aim of maximizing the total revenue of all taxis in the long run. To achieve this, we divide the city into uniform hexagonal grids, as opposed to square grids used in previous studies [21, 6].
**B**... | ACB | ABC | CBA | BCA | Selection 3 |
**A**: The idea that optimal prediction intervals should saturate inequality (2) and minimize the average size was dubbed the High-Quality (HQ) principle by Pearce et al. pearce2020uncertainty ; pearce2018high **B**:
As stated before, there are two quantities that are mainly used to evaluate the performance of interva... | CBA | ACB | CAB | BAC | Selection 4 |
**A**: We can further differentiate Bar(new) and Bar(cont), representing respectively the beginning of a new bar and a continuation of the current bar and always have one of them before a Sub-bar token. This way, the tokens would always occur in a group of four for MIDI scores.
For MIDI performances, six tokens would b... | BCA | BAC | ACB | CAB | Selection 4 |
**A**: Thus, if in the next iteration we start at exactly the neighbor of the previous central vertex, there can be only O(n)𝑂𝑛O(n)italic_O ( italic_n ) such jumps in total.
**B**: As it was stated in the proof of Lemma 2.2, while searching for a central vertex we always jump from a vertex to its neighbor in a way t... | ABC | ABC | ACB | CBA | Selection 4 |
**A**: From the figure, the proposed DeepSC-SR can provide lower WER scores and outperform the speech transceiver under various channel conditions, as well as the text transceiver under the Rayleigh channels when SNR is lower than around 8 dB. Moreover, similar to the results of CER, DeepSC-SR obtains good WER scores o... | ABC | ABC | CBA | BAC | Selection 3 |
**A**: However, the cross-branch can still produce reasonable scores as the network is learned to only propagate features from the same category for each point. The intra branch produces better results than the cross branch, but still lower than the basic branch**B**: This supports our argument that the ISFR module imp... | BCA | BAC | CBA | ACB | Selection 1 |
**A**:
Figure 5: Qualitative results of our method for multi-class 3D object detection**B**: We use orange box for cars, purple box for pedestrians, and green box for cyclists**C**: All illustrated images are from the KITTI test set. Zoom in the image for more details. | ACB | BCA | ACB | ABC | Selection 4 |
**A**: Moreover, failure cases may happen on some text-like objects or super-tiny texts, which are also common challenges for other state-of-the-art methods [10, 21, 20]. Examples of such failure cases are shown in Fig. 9.
**B**: A potential solution may be to reason according to the semantic information of text**C**: ... | ACB | CBA | BCA | BAC | Selection 2 |
**A**: Specifically, the two proposed methods present two different relationship mapping mechanisms between memory blocks and IP addresses to strike a balance between computational cost and memory use. They can be employed to search for frequently occurring IP addresses in practical applications. The extensive experime... | ACB | BCA | CAB | CBA | Selection 4 |
**A**: When
the system matrix in (2) is extended to the n𝑛nitalic_n-tuple case, it corresponds to the linear system resulting from the domain decomposition method for elliptic**B**: The above 3333-by-3333 block linear problems (1) and (2) can be naturally extended to the n𝑛nitalic_n-tuple cases**C**: For example, whe... | CBA | BCA | CAB | BAC | Selection 3 |
**A**: A hub itself does not contain data but facilitates training by coordinating clients’ information.
The goal is to jointly train a model on the features of the data contained across silos, without explicitly sharing raw data between the clients and the hubs and between clients across different silos.**B**: The hub... | BCA | CBA | ACB | CAB | Selection 2 |
**A**: These theorems are based on the T-Schur decomposition and can be regarded as generalizations of the Bauer-Fike theorems for non-diagonalizable matrices given in 1986Generalization ; Golub2013matrix **B**: The detailed proof are provided in the appendix section.
**C**: In the following, we present two general cas... | CBA | CBA | BCA | ABC | Selection 3 |
**A**: The Bi-GFF module is developed to enhance the consistency of the rebuilt structures and textures**B**:
On Bi-directional Gated Feature Fusion**C**: For the results obtained using a simpler fusion module (a channel-wise concatenation followed by a convolution layer), blurred edges and unexpected noise can be obs... | BCA | BCA | BAC | ABC | Selection 3 |
**A**: Together with the binary symmetric channel (BSC), they are frequently used in coding theory and information theory because they are among the simplest channel models, and many problems in communication theory can be reduced to problems in a BEC. Here we consider more generally a q𝑞qitalic_q-ary erasure channel ... | CAB | BAC | CBA | ABC | Selection 3 |
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