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**A**: Response-based KD methods [19, 58, 3] have the natural property of hiding models.
Hinton et al**B**: [58] decouple the KLD into two uncorrelated losses and combine them by weighted summation.**C**: [19] use Kullback-Leibler Divergence (KLD) between the softened logits of teacher and student models as the loss to... | ACB | BCA | CAB | CAB | Selection 1 |
**A**: It is described in Section 4**B**: However, it is possible to decrease aliasing error even when FNO is used for interpolation. One simply needs to train (or fine-tune) on a sufficiently fine grid. The decrease of aliasing error in this scenario is illustrated in the left plot of Figure 2a.**C**:
Our solution to... | BCA | ABC | ACB | BAC | Selection 1 |
**A**: COCOStuff10k [2]**B**: It contains 9k training samples and 1k validation samples. We report the mIoU to evaluate our method.
**C**: The challenging dataset is developed on MSCOCO [26] by adding dense pixel-wise stuff label, resulting in 172 classes: 80 for thing, 91 for stuff, and 1 for unlabeled | BAC | BAC | CBA | ACB | Selection 4 |
**A**: For more detail, see the subspace clustering section from the recent survey by Liu et al. [27].**B**: provide a feature learning method and visualization tool to explore non-axis aligned subspaces using a series of UMAP projections to embed the data [14]**C**: Tatu et al. [34] use the SURFING [5] algorithm to pr... | BAC | BCA | CBA | BCA | Selection 3 |
**A**:
By integrating the two levels of representation learning, that is, (i) feature learning at each step and (ii) embedding learning across multiple steps, we propose a sample-efficient algorithm, namely Embed to Control (ETC), for POMDPs with infinite observation and state spaces**B**: To this end, we construct a ... | CAB | ACB | ABC | CBA | Selection 2 |
**A**: The first line of research studies offline RL in standard MDPs without any partial observability**B**: Table 1: We compare with most related representative works in closely related lines of research**C**: The second line of research studies online RL in POMDPs where the actions are specified by history-dependent... | ACB | ACB | BCA | BAC | Selection 4 |
**A**: Without constraints, one can apply stochastic gradient descent (SGD) and its many variates, whose statistical properties (e.g., asymptotic normality) have been comprehensively studied from different aspects (Robbins1951stochastic; Kiefer1952Stochastic; Polyak1992Acceleration; Ruppert1988Efficient). However, unli... | ACB | ACB | CAB | CBA | Selection 3 |
**A**: Another open problem is the analysis of isoparametric generalized Taylor-Hood families in 2D and 3D to cope with curved boundaries. Perturbation arguments similar to those used in [6], [7] for isogeometric generalized Taylor-Hood families seem to be a promising approach for this open problem.**B**: The analysis ... | ACB | BCA | CBA | BAC | Selection 3 |
**A**: All models use ImageNet-1k pretrained weights unless otherwise stated**B**:
Table 2: Results for semantic segmentation on Cityscapes validation set show SOTA mIoU (single scale) by WaveMix without compromising inference throughput (FPS)**C**: * implies no pre-training on ImageNet-1k. | BAC | ACB | BCA | BCA | Selection 1 |
**A**:
We may indeed rewrite the parametrization Ξh=H~h(Ξ0,…,Ξh−1)subscriptΞℎsubscript~𝐻ℎsubscriptΞ0…subscriptΞℎ1\Xi_{h}=\tilde{H}_{h}(\Xi_{0},\ldots,\Xi_{h-1})roman_Ξ start_POSTSUBSCRIPT italic_h end_POSTSUBSCRIPT = over~ start_ARG italic_H end_ARG start_POSTSUBSCRIPT italic_h end_POSTSUBSCRIPT ( roman_Ξ start_POST... | ACB | CBA | ABC | CBA | Selection 3 |
**A**:
We have described the path to the state-of-the-art for five representative areas considering the relationship between natural and mathematical language, either through necessity of the task or efficacy of approach. We describe the details, limitations and successes within each area and find that informal method... | BAC | ACB | CBA | CBA | Selection 2 |
**A**: Within the SBM framework, the closest work to ours is the strata multilayer SBM (Stanley et al.,, 2016), in that it looks for both common connectivity patterns and network clustering. However, it does not consider a collection of networks but a multiplex network where all the networks share the same nodes.**B**:... | CBA | BAC | ABC | BCA | Selection 1 |
**A**: (left) Predictions of rotation angle vs. the ground truth (normalized to [−1,1]11[-1,1][ - 1 , 1 ]) in test set**B**:
Figure 5: Performance of FactorNets for individual rotation learning**C**: (right) Distributions of absolute percentage errors (in %) of all data points in the dataset. | BAC | BCA | CBA | CAB | Selection 1 |
**A**: On PU classification task, contrastive pretraining with puNCE results in large improvement over current state-of-the-art PU learning algorithms (Table 5).
**B**: Across all the setting, puNCE consistently outperforms supervised and unsupervised contrastive losses and is especially powerful when available supervi... | BAC | CAB | CBA | ABC | Selection 3 |
**A**: Specifically, we see evidence that each of these different types of social affiliation and support (between 7 to 12 depending on the network, see Appendix H for information on the different types of relationships in each network) are all well modeled by the same generative process**B**: If, in a future study, th... | CAB | BCA | ABC | CAB | Selection 2 |
**A**: johri2021nearest ,
and where necessary, the larger IonQ Aria machine with a capacity of 32 physical and 20 algorithmic qubits (ionq2022aria, )**B**: Experiments below used the 11-qubit trapped ion quantum computer described by Johri et al**C**: A crucial point to bear in mind with quantum computing is that the m... | BAC | ACB | CBA | CAB | Selection 1 |
**A**: 2020), Geom-GCN (Pei et al. 2020), BernNet (He et al. 2021) and PPGNN (Lingam et al**B**: Günnemann 2019). Five recent GNNs that target heterophilic graphs are also listed as baselines: GPRGNN (Chien et al. 2021), H2GCN (Zhu et al**C**: 2021). We run experiments on six real-world graphs: Texas, Cornell, Wisconsi... | BCA | ABC | BCA | BAC | Selection 4 |
**A**: In some contexts, the benefits of the reduced risk among subpopulations may outweigh possible harms from segregation**B**: In short, this work analyzes natural dynamics with consequences for the distribution of subpopulations amongst independent learners; whether or not the consequences are desirable depend on t... | ABC | ABC | ACB | BCA | Selection 3 |
**A**: We note that unfairness may ensue using this classification method due to differences in health literacy between states**B**: Out of the 18 cancer types, classifiers for 8 types had an overall positive prediction rate of more than twice or less than half of the true positive rate. We removed these classifiers fr... | ABC | CAB | BCA | ABC | Selection 3 |
**A**: By qualitative and quantitative evaluation on six real-world and 25 semi-synthetic use cases, we showed that the approximate algorithm produces better motifs than all its competitors at lower runtimes, and that its results come very close to the exact algorithm despite an exponentially lower runtime**B**: We env... | BCA | ACB | BAC | ABC | Selection 2 |
**A**: [23]-[24] and references therein**B**:
In reality, many learning tasks process very large datasets, and thus decentralized parallel processing of data by communicating and computing units in the network is necessary, see e.g**C**: Besides, if the data contains sensitive private information (e.g. medical and soc... | ACB | CAB | CAB | BAC | Selection 4 |
**A**:
Figure 4 reports the iteration count and computation time required for each compared solver at various matter densities**B**: These reported results are averaged over 30 runs**C**: Figure 4a confirms that AA and Alternating AA require significantly fewer iterations to reach the prescribed 10−10superscript101010... | BAC | CAB | ABC | BAC | Selection 3 |
**A**: At the same time, the majority of existing models for topic-controllable summarization either incorporate topic embeddings into the model’s architecture [5, 6] or modify the attention mechanism [9, 10]**B**: Even though control tokens have been shown to be effective and efficient for controlling the output of a ... | ACB | BCA | CBA | BCA | Selection 1 |
**A**:
Our tiles are useful, for example, for Toffoli+H circuits. Toffoli gates cannot, in general, be executed natively, and are decomposed into sequences of Clifford+T gates**B**: A Toffoli gate can be obtained from an AND gate and a controlled-S gate. When using measurement-based uncomputation [30] and an ancilla, ... | ACB | CAB | BCA | ABC | Selection 1 |
**A**:
For our training, we require the MRI scans in two different parameter settings of {TE, TR}. One serves as input to the model, and the other as the ground truth corresponding to the desired parameter setting to compute the loss. We use MRiLab [7] which is an MRI Simulator to generate these synthetic brain scans ... | BCA | ACB | BAC | BAC | Selection 2 |
**A**: Section 2 reviews the EnVarA and some existing neural network-based numerical approaches for solving PDEs**B**:
The rest of the paper is organized as follows**C**: Section 3 of the paper is devoted to the development of the proposed EVNN schemes for L2superscript𝐿2L^{2}italic_L start_POSTSUPERSCRIPT 2 end_POST... | ACB | BAC | BCA | ABC | Selection 2 |
**A**: In this subsection, we study the general properties of such metrics.
**B**: Here, the probability measures are replaced by sheaves and the integration of real-valued functions against the probability measure by the pushforward of the sheaves by such functions**C**: In this section, we elaborate on our study of p... | BCA | BAC | ABC | CBA | Selection 4 |
**A**: TABU is a variant of HC and is, as one might expect, sensitive to variable ordering, but the mean F1 change of 0.278 is considerably less than that of 0.412 for HC**B**: Figure 5 shows the sensitivity to variable ordering for the algorithms described in section 2 and compares it with their sensitivity to other s... | BAC | ACB | ACB | ABC | Selection 1 |
**A**: Using this result, our construction can be modified in such a way that eventually we get a Dist-Nonhalt instance on a simple graph, at the expense of having a higher number of vertices. Such a reduction, assuming the Planted Dense Subgraph Conjecture, still implies a polynomial hardness to the problem.
**B**: no... | ACB | CAB | CAB | CBA | Selection 4 |
**A**: VI. Because this method can make full use of the powerful temporal information, the Inception Score (IS) shows SOTA results compared to the original FSVAE [69] and the same structure ANN. And our TCJA image decoding outperforms better on all metrics for CIFAR10 datasets. Moreover, results on CelebA and MNIST are... | ABC | CBA | BCA | ACB | Selection 3 |
**A**: Given**B**: Ω=(0,12)2Ωsuperscript0122\Omega=\bigl{(}0,\frac{1}{2}\bigr{)}^{2}roman_Ω = ( 0 , divide start_ARG 1 end_ARG start_ARG 2 end_ARG ) start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT**C**: Since all four corners
of ΩΩ\Omegaroman_Ω are π/2𝜋2\pi/2italic_π / 2, we have μ=(1,1,1,1)𝜇1111\mu=(1,1,1,1)italic_μ = (... | CAB | ACB | ABC | BAC | Selection 1 |
**A**: Rather, they serve as user interfaces for interacting with the protocol. The vulnerability may reside in any contract invoked through nested calls originating from these action candidates. If FlashFind is not utilized, users can consult the protocol documentation to identify the appropriate user-interface contra... | CBA | ABC | ACB | CAB | Selection 4 |
**A**: Interestingly, our bounds have a linear dependence on the horizon, compared to the exponential dependence in [33], but have an exponential dependence on the dimension of the prior distribution, corresponding to the exponential dependence on dimensionality of KDE. We argue, however, that in many practical cases t... | CAB | BCA | ACB | BCA | Selection 1 |
**A**:
Moreover, when we set δ𝛿\deltaitalic_δ at 0.6 (where F1subscriptF1\text{F}_{1}F start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT performs the best), the correct ratio 131313The correct ration is calculated by dividing number of relevant items by number of retrieved items**B**: of EN →→\rightarrow→ ZH and ZH →→\rightarr... | CAB | BCA | ABC | CAB | Selection 3 |
**A**: 4 (b). Other details of the architecture and training procedure are provided in Sec. IV.3.**B**:
In this work, we employ a ViT pre-trained on the ImageNet-21k dataset from the authors Dosovitskiy et al. (2020); Steiner et al. (2021); Wightman (2019) and then fine-tune the model for predicting human facial attr... | ABC | BCA | BCA | CAB | Selection 4 |
**A**: FuSeBMC v3 achieved first place in Cover-Error, fourth place in Cover-Branches, and placed second overall in Test-Comp 2021, while FuSeBMC v4 reached first place in both categories and overall in Test-Comp 2022**B**: However, considering the test generation task set has been significantly expanded in Test-Comp 2... | ABC | BAC | CAB | BCA | Selection 4 |
**A**: To derive (29), we apply Proposition 6 to the JFP basis, then**B**: The first equation (28) follows immediately from the commutativity of fractional (and integer-order) integration matrices stated in (3)**C**:
The bandwidths of the matrices follow from Lemmas 7 and 8 | BAC | CBA | CAB | BCA | Selection 2 |
**A**: find that the indexes based on common neighbors fail to identify missing links in the tree-like networks [45]**B**: To solve this problem, they take advantage of network heterogeneity and propose the heterogeneity index (HEI). The HEI is defined as
**C**: Shang et al | ACB | ABC | BCA | CAB | Selection 3 |
**A**: We grid search for the best k𝑘kitalic_k on Cars dataset: k=𝑘absentk=italic_k =36 for MobileNetV2, 39 for ProxylessNAS, 12 for MCUNet, and apply it to all datasets.) as the baseline upper bound (denoted as "upper bound")**B**: Interestingly, our sparse update achieves a better downstream accuracy compared to th... | CBA | BAC | ABC | BCA | Selection 4 |
**A**: A=(aij)𝐴subscript𝑎𝑖𝑗A=(a_{ij})italic_A = ( italic_a start_POSTSUBSCRIPT italic_i italic_j end_POSTSUBSCRIPT ) is called an M𝑀Mitalic_M-matrix if**B**: Let A=(aij)𝐴subscript𝑎𝑖𝑗A=(a_{ij})italic_A = ( italic_a start_POSTSUBSCRIPT italic_i italic_j end_POSTSUBSCRIPT ) and N={1,2,…,n}𝑁12…𝑛N=\{1,2,\ldots,... | CAB | BCA | CBA | BAC | Selection 1 |
**A**: We note that the sorting problem with the Ham fitness function regarded in [STW04] is exactly what we obtain from applying this construction to the classic OneMax benchmark**B**: We are not aware of any other classic benchmark for which the permutation-based variant (as constructed above) has been analyzed so fa... | CBA | CBA | BCA | BAC | Selection 3 |
**A**:
where BI(v)𝐵𝐼𝑣BI(v)italic_B italic_I ( italic_v ) is the standard bilinear interpolation operator**B**: We used PyLops222https://github.com/PyLops/pylops, the linear operator library for Python, for performing bilinear interpolation and its transpose operation**C**: Consequently, the grid transfer operator... | CBA | BAC | ABC | BCA | Selection 3 |
**A**: However, even if a monotone network can approximate a monotone function arbitrarily well, it might require a much larger size when compared to unconstrained networks**B**:
We have shown that, with 4444 layers, monotone networks can serve as universal approximates**C**: In this case, the cost of having a much la... | BCA | CAB | BAC | CBA | Selection 3 |
**A**:
QMC methods are well-suited for large scale computations since they are trivial to parallelize**B**: Randomization of QMC rules also enables the computation of practical error estimates. These features make QMC methods ideal for heavy-duty uncertainty quantification compared to regular Monte Carlo methods (slow... | ABC | ACB | ABC | BAC | Selection 2 |
**A**: At each time step, an agent selects a channel and attempts to transmit a message. If the message is successfully delivered, the agent receives a reward of 1111; otherwise, the reward is 00**B**: This corresponds to the bandit setting. Since mobile devices sit at different geographic locations, the channel availa... | CBA | ACB | ACB | BCA | Selection 4 |
**A**: It is noteworthy that the aforementioned algorithms are applicable in (strongly) convex cases. However, within the nonconvex nonsmooth setting, the algorithm proposed by [71] stands out with global convergence guarantees when the nonsmooth term is convex.
**B**: To attain a superlinear convergence rate, the IQN ... | ABC | ABC | ABC | CAB | Selection 4 |
**A**: Our algorithm is a generalization of those proposed in ([37, 38]) from matrices to tensors. To the best of our knowledge, this is the first fixed-precision algorithm proposed for the t-SVD333After acceptance of the paper, the author found similar incremental algorithms for computation of the t-SVD in [39, 40].. ... | CAB | BCA | CBA | ABC | Selection 3 |
**A**:
We statistically analyze the results following the recommendations of [12]**B**: We use the non-parametric Wilcoxon paired signed-rank test [48] for the comparison of the predictive performance of the two methods over multiple datasets**C**: We set the significance level to 0.05 in all the experiments. | BAC | ABC | BCA | CAB | Selection 2 |
**A**: Thus, it can properly estimate the waypoints which are also laid in BEV space. Although AIM-MT predicts four waypoints and DeepIPC only predicts three waypoints, it is still considered a fair comparison because the MAE formula averages the error across all predictions**B**:
In the waypoints prediction task, Dee... | BAC | CAB | ABC | ABC | Selection 1 |
**A**: Since every independent set intersects each of the clique-bags in at most one vertex, dynamic programming still computes maximum weight independent sets in such graphs in polynomial time even if the bags could be arbitrarily large.**B**: For example, suppose that every bag of the decomposition is a clique, that ... | CBA | BCA | ACB | BCA | Selection 1 |
**A**: However, these methods lack exact privacy budget evaluations, providing only empirical utility under different levels of noise**B**: Additionally, ranbaduge2022differentially perturbs local model weights to satisfy DP. However, it requires bounding the sensitivity of each layer’s weights in the local model.
To ... | ACB | ABC | BCA | CBA | Selection 3 |
**A**: There are mainly two lines of methods in reinforcement learning [72, 73]: policy-based methods and value-based methods**B**: Value-based methods, such as DQN [74] and SARSA [75], aim to maximize the expected total reward and take actions according to the expected rewards of actions. Policy-based methods, includi... | CAB | ACB | BCA | CAB | Selection 3 |
**A**: Chunshui Cao
received the B.E. and Ph.D. degrees from University of Science and Technology of China in 2013 and 2018, respectively**B**: During his Ph.D. study, he joined Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Aca... | ACB | BAC | ABC | CBA | Selection 3 |
**A**:
Overall 222The project resources are in the GitHub https://github.com/changtianluckyforever/version_one., our main contributions are as follows: (i) This is the first work to investigate and handle the overestimation problem of the reinforcement learning framework for task-completion dialogue systems**B**: (ii)... | BCA | BAC | ABC | CAB | Selection 3 |
**A**:
We also compare the influence of predicting intention correctly to the accuracy of action classification, illustrated in Table 3**B**: The results show that there is a significant and direct relationship between noun and intention**C**: By conditioning the action-level prediction framework through the intention... | BAC | ABC | ACB | ACB | Selection 2 |
**A**: It calibrates one-class representations by adaptively penalizing uncertain predictions while at the same time emphasizing confident predictions**B**: In UMC, a novel calibrated one-class loss function is proposed, in which a prior (i.e., Gaussian) distribution is imposed on the one-class distances and utilized t... | BAC | ACB | ABC | BCA | Selection 1 |
**A**: Similarly, to evaluate narrative generation in open domain settings with sentences that can be logically inferred from mathematical operations over the input table, Chen et. al. (Chen et al., 2020a) modify the reference narratives of the TabFact dataset to construct LogicNLG with 7392 tables. Following this, wit... | ACB | BAC | BCA | CAB | Selection 3 |
**A**: In the case of online training, it requires twice the GPU space compared to the baseline**B**: However, with offline training, there is no additional GPU consumption overhead. Additionally, since certain hyperparameters have varying impacts on different predicate categories, it becomes challenging to achieve the... | BCA | CAB | BAC | CAB | Selection 1 |
**A**: Thus, a PoW-based blockchain system can have multiple attackers**B**:
Multiple Attackers: Except for honest miners, a miner who finds a new block can launch PSM or A-PSM attacks simultaneously**C**: The evaluation results indicate that in PSM, the variance of mining power among different attackers has no impact... | CBA | BCA | BAC | BCA | Selection 3 |
**A**:
Figure 7: Learning rate warmup gradually reduces the preconditioned sharpness during optimization. Left: The evolution of the preconditioned sharpness for a 6L-6L encoder-decoder Pre-LN transformer trained on the WMT En=>De task at η=.001𝜂.001\eta=.001italic_η = .001 with a linear warmup period of 40000 steps*... | BAC | BCA | BCA | ABC | Selection 4 |
**A**: For the two atomistic systems, alanine dipeptide and chignolin, we describe the systems using two different types of high-dimensional representations (distances and dihedral angles, respectively) to show that the framework can work regardless of the chosen configuration variables.**B**: For the stochastic embedd... | ACB | CBA | ACB | BAC | Selection 2 |
**A**: It is unknown whether these procedures work well on data sets for which they were not created; this is an active area of investigation**B**: The radius data are noisy which negatively impacts the accuracy of any radius based filtering methods. Developing smoothing algorithms for vessel radius is worthwhile, quan... | CBA | BCA | CAB | BAC | Selection 2 |
**A**: The training, validation and test sets are produced by a packet level simulator (OMNeT++ v5.5.1 [25]). Though lacking of other benchmark dataset, this dataset includes a summary of patterns of real-world network topologies from The Internet Topology Zoo [26]**B**: Dataset. We verify the performance of the model ... | ABC | CBA | ABC | BAC | Selection 4 |
**A**: (2020) for arbitrary ι>0𝜄0\iota>0italic_ι > 0.**B**: The ι𝜄\iotaitalic_ι-CCE can be found efficiently by the method developed in Xie et al**C**:
An ι𝜄\iotaitalic_ι-CCE may not have mutually independent marginals since the two players take actions in a correlated way | BAC | CAB | CBA | CAB | Selection 3 |
**A**: The importance sampling scheme could be further improved by solving an optimal control problem minimizing the level-difference estimator variance rather than the single-level DLMC estimator (see Remark 9)**B**: The multilevel DLMC algorithm could be optimized for determining the optimal parameters τ𝜏\tauitalic_... | BCA | CAB | CBA | BAC | Selection 1 |
**A**: Nevertheless, they rapidly decrease in the orbital operation, with the mean remaining a few meters and millimeters per second for the position and velocity, respectively. The means of the samples for estimating the constant parameters tend to be the real value of each of them, as can be checked in Figures 5d and... | ABC | CAB | BAC | CBA | Selection 2 |
**A**: To the best of our knowledge, this introduces a novel geometric perspective on the problem of computing Brascamp–Lieb constants for simple input data.**B**:
In this paper, we introduced a novel fixed-point approach for computing Brascamp–Lieb constants, which is grounded in nonlinear Perron–Frobenius theory**C*... | ACB | BCA | ACB | CAB | Selection 4 |
**A**: Next, we refine this model by applying a weight-based filtration. Filtration progressively removes less important edges, resulting in a nested sequence of increasingly coarser simplicial complexes. Finally, we leverage the concept of simplicial homology to analyze these filtered complexes**B**: Simplicial homolo... | BAC | ABC | BCA | ABC | Selection 3 |
**A**:
Figure 1**B**: Comparison between the typical semi-supervised learning (SSL) and the semi-supervised domain generalization (SSDG)**C**: Note that different colors denote different domains. In the SSDG setting, there are multiple training domains with different data distributions when compared with SSL. | CBA | CAB | ABC | CBA | Selection 3 |
**A**: Given the above challenges, we design our Conv-Adapter as a bottleneck structure, which is also widely used by PET methods of NLP tasks [19, 21]**B**: Precisely, it consists of two convolutional layers with a non-linearity function in-between.
The first convolution conducts channel dimension down-sampling with a... | CBA | ABC | BAC | ACB | Selection 4 |
**A**: Figure 3 shows the performance of CP-PINNs in discovering changepoints and solving (16). Specifically, the leftmost panel illustrates the precise solution across a uniform temporal scale**B**: This approach facilitates the visualization of PDEs solutions as distinct entities based on the locations of changepoint... | CAB | ACB | CBA | CBA | Selection 2 |
**A**: Indeed, while many of the referenced works above at least verbally express the notion that in many inverse problems, we “know more” about the parameters in some parts of the domain than in others, we have not found published works that try to provide quantitative measures of this concept**B**:
The differences i... | ACB | CBA | CAB | BAC | Selection 4 |
**A**:
On mouseover, the image is shown**B**: A lasso selection can be used at the points to select a set of images for the labeling process**C**: The selected points are increased in size to better highlight them. The re-annotation space is accessed from a button in the left-hand drawer. The current state of the grap... | BCA | CAB | ABC | CBA | Selection 3 |
**A**: All datasets of SuperGLUE are collected into source datasets, i.e. BoolQ, CB, RTE, WIC, WSC, COPA, Multirc and Record. Furthermore, to enrich the types of source datasets, some other NLU benchmarks are also added, including extractive QA (SQuAD 2.0 [53]), named entity recognition (NER) (CoNLL03 [54], CoNLL04 [55... | CAB | BAC | ABC | BCA | Selection 1 |
**A**: Second, submissions may be reported to multiple archives to broaden their visibility. We de-duplicate across and within archives by hashing their content (see Appendix §G)**B**: Further steps are performed to enhance data reliability. First, many on-hold submissions are valid but were never verified; we perform ... | ABC | BAC | BCA | BCA | Selection 2 |
**A**: Notably, the strategy demonstrates remarkable efficacy in the context of improving the transferability of a single specific sample, a scenario of particular relevance to black-box attackers**B**: The findings reveal that attackers are not ‘stuck’ with a resulting adversarial example and its likelihood of transfe... | BAC | ABC | BCA | ACB | Selection 3 |
**A**: However, a different issue arises here due to the non-local correlations between the qubits. Namely, the entanglement of the encoded state is another potential source of concentration. Intuitively, this follows from the fact that tracing out qubits in very entangled encoded states, leads to local states that are... | BCA | CBA | ACB | BAC | Selection 2 |
**A**: X3D, a single pathway architecture also extended from a 2D image network**B**: The most complex X3D model X3D-XL only has 11 million parameters, which is over 5 times less than SlowFast. Therefore, X3D is remarkably efficient. However, X3D-XL achieves better performance (81.9% top1 accuracy) than SlowFast on the... | ACB | BAC | ABC | BCA | Selection 4 |
**A**: II00\displaystyle 011\displaystyle 11Uncertainty Score (GCJ)Norm.Imit.Obf. IObf**B**: Norm.Imit.Obf. IObf**C**: II00\displaystyle 011\displaystyle 11Uncertainty Score (GH)Abuhamad et al.Caliskan et al.Original
Figure 6. Anonymization performance (uncertainty score) in the | CAB | BAC | CAB | BCA | Selection 2 |
**A**: To make a valid comparison, we use the off-shelf dataset miniImageNet (100 classes) as the large train set for ProtoNet, and use the train set in CoOp for the few-shot reference set for it**B**: We train ProtoNet on miniImageNet with 400 epochs and 8-way k𝑘kitalic_k-shot (k=1,2,4,8,16𝑘124816k=1,2,4,8,16italic_... | ACB | BAC | BCA | ACB | Selection 3 |
**A**: This stark divergence vividly underscores how the underlying content intricately molds the expressive nuances in artistic representation.
**B**: Consider the profound contrast between the drawing styles employed in a close-up portrayal of a human figure and those used to depict a vast, distant mountain vista**C*... | BCA | ABC | CBA | ABC | Selection 3 |
**A**: In order to preserve this approach on the discrete level, we consider here approximations by a monotone finite element method that satisfies a discrete maximum principle (see, e.g., [ciarlet1973maximum])**B**: The proof of uniqueness of weak solutions of (9) uses the Comparison Principle of elliptic operators (s... | ABC | BAC | CAB | BCA | Selection 2 |
**A**: The relation of the preallocated and finally allocated channel sets is depicted in Fig. 1.**B**:
The method proposed in [16] overcomes this problem by applying a preallocation of channels. Preallocation means that before the combinatorial auction, a non-exclusive assignment of channels to tenants is performed (... | BAC | CBA | CAB | BAC | Selection 3 |
**A**: (3) Number of heads n𝑛nitalic_n: The results report that 4-head attention achieves the best performance in predicting 12 and 48 timestamps**B**: The forecasting quality drops off with too many heads n=16𝑛16n=16italic_n = 16.
(4) Representation dimension d𝑑ditalic_d: The representation dimension of inputs and ... | CBA | BAC | CAB | ABC | Selection 4 |
**A**: 3a,b): a Gaussian distribution for β𝛽\betaitalic_β-VAE and a uniform distribution for SWAE. Since the actual ground-truth variables do not follow either a single Gaussian or a uniform distribution, these models undoubtedly fail to learn meaningful representations**B**:
In Figure 3, we present a comparison betw... | ABC | ACB | BAC | BCA | Selection 3 |
**A**: Those theoretical models, for the sake of tractability, often ignore certain effects, such as nonlinearities of the motor or the influence of temperature on the electric resistances**B**: Another approach consists of measuring the electric consumption of the WMR under different conditions, creating a dataset, an... | ACB | CAB | BCA | CBA | Selection 3 |
**A**: The assumptions are also sufficiently broad to apply for most mixtures but they can presumably be weakened further**B**: For a given mixture model, Proposition 4 is relatively easy to apply because the mapping from standard parameterization to natural parameters is usually known and known to be invertible.**C**:... | ABC | ABC | BCA | BAC | Selection 3 |
**A**: We begin in Section 4.1 by analysing the problem of counting k𝑘kitalic_k-matchings in somewhere dense host graphs, and proving Theorem 2; this is the most technical part**B**:
This section is devoted to the proofs of Theorem 2, Theorem 4, and Theorem 3**C**: We then move on to prove Theorem 4 and Theorem 3 in ... | CAB | ACB | BAC | CBA | Selection 3 |
**A**: Counterfactual inference is also used to addresses the clickbait issue, which estimates the direct effect of exposure features in the prediction and removes it from recommendation scores [41].
**B**: [12, 40] uses multi-task learning to estimate the contribution of each cause and performs counterfactual inferen... | CBA | CAB | ACB | CAB | Selection 1 |
**A**: Yang et al**B**: In [15, 16], the quality-aware features including the picture complexity, brightness, sharpness, and**C**: adopted a fully convolutional network to separate the image into structural regions and texture regions and extracted the structural features and perceptual features from the two regions fo... | CAB | ACB | BAC | CAB | Selection 2 |
**A**: To further characterize the datasets and distinguish different sorts of heterophily, we propose a new measure called label informativeness.
**B**: Based on this framework, we suggest using adjusted homophily to measure whether similar nodes tend to be connected**C**: In summary, we propose a theoretical framewor... | BAC | CAB | CBA | ABC | Selection 3 |
**A**: In fact, the proof of (1.4) does not even require strictly positive Ricci curvature lower bounds, i.e**B**:
Following the argument in [CEL12, Proof of Theorem 1], one can prove a quantum analogue of (1.4) using similar properties of quantum depolarizing semigroups**C**: Lemma 2.4 can be weakened. We will not di... | ABC | BCA | CBA | BAC | Selection 4 |
**A**: As summarized in Table 4, CPA-Boot succeeds in detecting all different instances of these adversarial behaviors where the BSP returns error-specific alarms to abort the boot process. We show below how the adversarial behaviors are implemented and how CPA-Boot detects them.**B**: In particular, we introduce an ex... | ABC | CBA | ACB | BCA | Selection 2 |
**A**: An alternative assumption where the lower and upper bounds are functions of ‖z‖norm𝑧\|z\|∥ italic_z ∥ will be considered later.
**B**: Assumption 12 requires boundedness on the sum of two storage functions in terms of parts (but not all) of their arguments**C**: This resembles boundedness on a time-varying Lyap... | BCA | BCA | ACB | CAB | Selection 4 |
**A**:
In this paper, we propose a way of analyzing safety probability for a stochastic system via a CBF approach. The contributions of this paper are as follows**B**: First, we propose an almost sure reciprocal control barrier function (AS-RCBF) ensuring the safety of a set with probability one, which is considered a... | BAC | ACB | ABC | CAB | Selection 3 |
**A**: Despite these initial positive results, there were some situations where the legible motions caused some confusion on the users, because the participants’ perspective led them to believe that the movement was towards another person. So in a posterior work [6], the authors used the insights from Nikolaidis et al.... | CBA | BAC | BAC | BCA | Selection 1 |
**A**: x𝑥xitalic_x). All random variables take values in some alphabets that are in calligraphic letters (e.g. 𝒳𝒳\mathcal{X}caligraphic_X).
We shall restrict our attention to finite alphabets only.**B**: Random variables are in capital case (e.g**C**: X𝑋Xitalic_X), and their realization are in lower case (e.g | BAC | CBA | ABC | CAB | Selection 4 |
**A**: In this case, though the terminal voltage magnitude is well regulated, it remains unclear if the GFL converters can be considered as effective voltage sources to enhance the power grid (voltage) strength**B**:
Moreover, one important question is: since GFL converters can perform constant AC voltage magnitude co... | CAB | BAC | CAB | CBA | Selection 2 |
**A**: Fig. 9 shows three annual temperature anomaly series from distinct regions: the Northern Hemisphere, the Southern Hemisphere and the Tropics from 1850 to 2021, which are described in detail in [19]**B**: The data are temperature anomalies relative to a reference period of 1961-1990 [19]. Each series consists of ... | CAB | BCA | ACB | BAC | Selection 2 |
**A**: fang2021learning conduct theoretical studies in a more general setting by extending the classical closed-set PAC framework valiant1984theory to open-set environments, deriving analytical bounds of the generalization error in the context of OSR**B**: liu2018open relates OSR to transfer learning and interprets ... | BAC | BAC | ACB | BCA | Selection 4 |
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