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**A**: 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 for all classical groups in odd characteristic in [11] and even characteri... | CBA | CAB | ABC | ABC | Selection 2 |
**A**: Except for (ii), all steps above above can be performed efficiently as the matrices involved are sparse and either local or independent of hℎhitalic_h**B**: Solving (25) on the other hand involves computing the hℎhitalic_h-dependent, global operator P𝑃Pitalic_P, leading to a dense matrix in (25)**C**: From now ... | ABC | BAC | CAB | BAC | 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,... | CBA | CAB | BCA | CBA | Selection 3 |
**A**: In order to capture these temporal variabilities, we build upon the Dynamic Series-Time Structure (DSTS) model (time series for short) for feature vector representation proposed in [20]**B**:
As observed in [19, 20], rumor features are very prone to change during an event’s development**C**: We base our credibi... | ABC | BAC | CBA | CBA | Selection 2 |
**A**: Assumption 1 includes many common loss functions, including the logistic, exp-loss222The exp-loss does not have a global β𝛽\betaitalic_β smoothness parameter**B**: and probit losses.
Assumption 1 implies**C**: However, if we initialize with η<1/ℒ(𝐰(0))𝜂1ℒ𝐰0\eta<1/\mathcal{L}(\mathbf{w}(0))italic_η < 1 / ca... | ACB | BAC | BAC | BAC | Selection 1 |
**A**: . As shown in Table 11, CreditScore is the best feature in general. Figure 10 shows the result of models learned with the full feature set with and without CreditScore**B**: Overall, adding CreditScore improves the performance, significantly for the first 8-10 hours. The performance of all-but-CreditScore jiggle... | CBA | CAB | ACB | ABC | Selection 4 |
**A**: In total, our training dataset for AOL consists of 1,740 instances of breaking class and 3,050 instances of anticipated, with over 300 event entities. For GoogleTrends, there are 2,700 and 4,200 instances respectively**B**: We then bin the entities in the two datasets chronologically into 10 different parts. We ... | ABC | CBA | BCA | CAB | Selection 3 |
**A**:
Table 1 shows basic patient information. Half of the patients are female and ages range from 17 to 66, with a mean age of 41.8 years**B**: Body weight, according to BMI, is normal for half of the patients, four are overweight and one is obese. The mean BMI value is 26.9**C**: Only one of the patients suffers fr... | BAC | BAC | ABC | CAB | Selection 3 |
**A**:
Overcoming these issues requires a higher-level scene understanding that models object interactions and predicts implicit gaze and motion cues from static images. Robust object recognition could however be achieved through more recent classification networks as feature extractors Oyama and Yamanaka (2018) at th... | BAC | ACB | BCA | BCA | Selection 2 |
**A**: In this work, we have answered several open questions about the string parameter of the locality number**B**: As an additional result, our reductions also pointed out an interesting relationship between these classical graph parameters and the locality number for strings, with implications for approximating thes... | CBA | ACB | CBA | BCA | Selection 2 |
**A**: (2017) show that learning predictive models of Atari 2600 environments is possible using appropriately chosen deep learning architectures. Impressively, in some cases the predictions maintain low L2subscript𝐿2L_{2}italic_L start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT error over timespans of hundreds of steps.
As lea... | ABC | BAC | BCA | ABC | Selection 2 |
**A**: The results highlight the superior energy efficiency of the proposed autonomous locomotion mode transition method for negotiating steps of different heights, in contrast to solely depending on the rolling locomotion mode. This underscores the efficiency of the proposed strategy in enabling energy-conscious step ... | CBA | ABC | BCA | ACB | Selection 1 |
**A**: Online bin packing has also been studied in the advice setting [12, 30, 2]**B**: In particular, it is possible to achieve a competitive ratio of 1.4702 with only a constant number of (trusted) advice bits [2]**C**: A restricted version of the bin packing problem, where items take sizes from a discrete set {1/k,2... | ABC | BCA | CBA | BCA | Selection 1 |
**A**: In [Schwartz & Ungar, 2015], it is highlighted that “language reveals who we are: our thoughts, feelings, belief, behaviors, and personalities”**B**: In particular, quantitative analysis of the words and concepts expressed in texts have played an important role in ADD. For instance, in [De Choudhury et al., 2013... | BCA | CBA | ABC | BAC | Selection 1 |
**A**: Hence, we do not compare with quantization methods in this paper.
We don’t use the warm-up strategy in the experiments**B**: In the experiments of (Lin et al., 2018), DGC gets far better performance on both accuracy and communication cost than quantization methods**C**: The momentum coefficient β𝛽\betaitalic_β ... | ABC | BCA | BAC | ABC | Selection 3 |
**A**: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization**B**:
, where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks**C**: operation. | CAB | BCA | BAC | ABC | Selection 3 |
**A**: Therefore, there is no waste of energy or time consumption between two iterations, which significantly improves learning efficiency**B**: The process of SPBLLA let UAVs free from message exchange**C**: All UAVs are altering strategies with a certain probability of ω𝜔\omegaitalic_ω, which is determined by τ𝜏\ta... | CAB | BAC | BCA | CBA | Selection 2 |
**A**: italic_g **B**: The symbol ∗*∗ between two matrices (e.g.,formulae-sequence𝑒𝑔e.g.,italic_e **C**: , C¯¯=A¯¯∗B¯¯¯¯𝐶¯¯𝐴¯¯𝐵\overline{\overline{C}}=\overline{\overline{A}}\,*\,\overline{\overline{B}}over¯ start_ARG over¯ start_ARG italic_C end_ARG end_ARG = over¯ start_ARG over¯ start_ARG italic_A end_ARG end_A... | ACB | ABC | BAC | BCA | Selection 3 |
**A**: It’s based on an incremental dynamic programming technique because of the step by step look-up table representation in which it determines the optimal policy[22]**B**: The Q-learning algorithm employs a table to estimate the optimal action value function, Q∗superscript𝑄Q^{*}italic_Q start_POSTSUPERSCRIPT ∗ end_... | CAB | BCA | ACB | ABC | Selection 2 |
**A**: (2019) trained a multi-task U-Net architecture to solve three tasks - separating wrongly connected objects, detecting class instances, and pixelwise labeling for each object, and evaluated it on a food microscopy image dataset. Other multi-task models have also been proposed for segmentation and classification f... | BAC | CAB | ABC | ABC | Selection 2 |
**A**: The results are shown in Figure 3 exemplarily for the Car, Covertype, and Wisconsin Breast Cancer (Original) dataset**B**: The other datasets show similar characteristics**C**: The overall evaluation on all datasets is presented in the next section.
The number of training examples per class is shown in parenthes... | ACB | ABC | BAC | ACB | 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.... | BAC | ACB | ABC | BAC | Selection 3 |
**A**:
However, training such discrete-valued DNNs555Due to finite precision of computer arithmetic, in fact any DNN is discrete-valued**B**: is difficult as they cannot be directly optimized using gradient-based methods.**C**: However, we use this term here to emphasize the extremely small number of values | ACB | BAC | CBA | CAB | Selection 1 |
**A**: More interestingly, among unbounded manifolds, Riemannian manifolds with strictly negative sectional curvature are hyperbolic spaces**B**: All compact Riemannian manifolds are trivially hyperbolic spaces**C**: Observe that “strictly negative” sectional curvature is a necessary condition (for example, consider th... | ACB | BCA | ABC | BAC | Selection 4 |
**A**: This means that projected points with high remaining costs can be moved by an additional optimization step**B**: Akin to this idea, t-viSNE might show a preview of the data points in the next optimization step. In consequence, users could determine whether the t-SNE optimization is completed or not, simply by ob... | BCA | CAB | CBA | CBA | Selection 1 |
**A**:
Benchmarks and comparison methodologies: The development of a novel bio-inspired solver includes the comparison to other techniques with several fitness functions**B**: Moreover, better comparison methodologies, including more attention to scalability and new statistical testing approaches such as the use of Ba... | CBA | BAC | ACB | ABC | Selection 3 |
**A**: The difference is which extra mechanism (such as attention, adversarial learning, graph sharpness, etc.) is used.**B**: To apply graph convolution on unsupervised learning, GAE is proposed [20].
GAE firstly transforms each node into latent representation (i.e., embedding) via GCN, and then aims to reconstruct so... | CBA | BAC | CBA | CAB | Selection 4 |
**A**: We then generate a set of packets to the server from spoofed IP addresses, belonging to the tested network. We probe the IPID value again, by sending packets from our real IP address. If the spoofed packets reached the server, they incremented the IPID counter on the server - an event which we infer when probing... | ABC | CBA | BCA | BCA | Selection 2 |
**A**: However, while metal oxide-based sensors are economical and flexible, they are unstable over time. Changes to the response properties of sensors make it difficult to detect and identify odors in the long term, and sensors have to be recalibrated to compensate [3]. Recalibration requires collecting and labeling n... | ABC | CAB | ACB | CBA | Selection 4 |
**A**:
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]**... | BCA | ACB | CAB | BAC | Selection 2 |
**A**: The VQA-CP dataset Agrawal et al. (2018) was introduced to study the robustness of VQA methods against linguistic biases**B**: Since it contains different answer distributions in the train and test sets, VQA-CP makes it nearly impossible for the models that rely upon linguistic correlations to perform well on th... | ABC | CBA | BAC | BCA | Selection 4 |
**A**:
We found that two LDA topics contained vocabulary corresponding with the OPP-115 category First Party Collection/Use, one dealing with purpose and information type collected and the other dealing with collection method**B**: Two LDA topics corresponded with the OPP-115 category Third Party Sharing and Collectio... | CBA | BAC | CAB | ABC | Selection 4 |
**A**: “The former approach is even more suitable for your VA system, because you use the accuracy of the base ML models as feedback/guidance to the expert in order to understand which instances should be wrangled”, said E3**B**: E2 stated that having an evaluation metric from early on is important for benchmarking pur... | BCA | BAC | BAC | ABC | Selection 1 |
**A**: Each user has 121 utterances on average. Weibo is a personalized dialogue dataset collected from Weibo conversations with 371/40/38 users for meta-training/meta-validation/meta-testing. Each user has 1200 utterances on average.**B**:
In Experiment II: Dialogue Generation, we use Persona [Zhang et al., 2018] and... | ACB | ABC | CAB | CBA | Selection 3 |
**A**:
The specialized codebook design of the DRE-covered CCA for multi-UAV mobile mmWave communications**B**: Under the guidance of the proposed framework, a novel hierarchical codebook is designed to encompass both the subarray patterns and beam patterns**C**: The newly proposed CA codebook can fully exploit the pot... | BAC | BCA | ABC | ACB | Selection 3 |
**A**: We**B**: Note that the 1111-color case with the completeness requirement is not very interesting, and also not useful for the general case: completeness states that every node on
the left must be connected, via the unique edge relation, to every node on the right – regardless of the matrix**C**: This will be boo... | CBA | CAB | CAB | BAC | Selection 1 |
**A**:
Contribution**B**: Going beyond the NTK regime, we prove that, when the value function approximator is an overparameterized two-layer neural network, TD and Q-learning globally minimize the mean-squared projected Bellman error (MSPBE) at a sublinear rate**C**: Moreover, in contrast to the NTK regime, the induce... | CAB | BAC | ABC | ACB | Selection 3 |
**A**:
As for the costs, the decoder depth has a strong impact on inference speed, as the decoder has to be computed once for each decoding step during auto-regressive decoding Kasai et al. (2021); Xu et al**B**: (2019); Li et al. (2022a); Chai et al. (2020) normally leads to faster inference speed than using both a d... | BCA | ACB | ABC | CAB | Selection 2 |
**A**: However, notice that the T0subscript𝑇0T_{0}italic_T start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT quotient of
Struct(σ)Structσ\operatorname{Struct}(\upsigma)roman_Struct ( roman_σ ) is sober when τ=τ⊆iτsubscriptτsubscript𝑖\uptau=\uptau_{\subseteq_{i}}roman_τ = roman_τ start_POSTSUBSCRIPT ⊆ start_POSTSUBSCRIPT itali... | BCA | ABC | ABC | CAB | Selection 1 |
**A**: There is a rich history of exploration in the field of distortion rectification. The most common method is based on a specific physical model. [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 para... | BCA | BCA | CAB | ACB | Selection 4 |
**A**: We can observe that in the small-batch training, SNGM and other large-batch training methods achieve similar performance in terms of training loss and test accuracy as MSGD.
In large-batch training, SNGM achieves better training loss and test accuracy than the four baselines**B**: Furthermore, it achieves faster... | CAB | BCA | CBA | BAC | Selection 2 |
**A**: We also consider the polynomial-scenarios model [23, 15, 21, 10], where the distribution 𝒟𝒟\mathcal{D}caligraphic_D is listed explicitly**B**: The most general way to represent the scenario distribution 𝒟𝒟\mathcal{D}caligraphic_D is the black-box model [24, 12, 22, 19, 25], where we have access to an oracle ... | ACB | BAC | CBA | ACB | Selection 2 |
**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. What’s more, multiplicative noises relying on the relative states between adjacent local optimizers make states, gr... | BCA | ACB | CBA | ACB | Selection 1 |
**A**: Generalization [8, 26] is one of the most widely used privacy-preserving techniques**B**: It transforms the values on QI attributes into general forms, and the tuples with equally generalized values constitute an equivalence group**C**: In this way, records in the same equivalence group are indistinguishable. k�... | BCA | CBA | ABC | CBA | Selection 3 |
**A**: During training process, the batch size is 8 (one image per GPU) and all BN statistics are freezed. Mixed precision training enables to reduce GPU memory. The input images are randomly resized to n×n𝑛𝑛n\times nitalic_n × italic_n, which is uniformly sampled from range [1200,1400]12001400[1200,1400][ 1200 , 140... | CAB | CAB | CBA | BAC | Selection 3 |
**A**: More specifically, we proved**B**:
In version 1 of this note, which can still be found on the ArXiv, we showed that the analogous version of the conjecture for complex functions on {−1,1}nsuperscript11𝑛\{-1,1\}^{n}{ - 1 , 1 } start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT which have modulus 1111 fails**C**... | CAB | ABC | ABC | BAC | Selection 1 |
**A**: However, the variance of MASTER is larger due to the random scheduling of multiple base algorithms. Our algorithm outperforms OPT-WLSVI in the abrupt change setting, but has worse performance in the gradual change setting, which agrees with the empirical findings in the nonstationary contextual bandit literature... | BAC | CBA | CBA | BCA | Selection 4 |
**A**: Singapore is a city-state with an open economy and diverse population that shapes it to be an attractive and vulnerable target for fake news campaigns (Lim, 2019)**B**: The rising attention of fake news in the local scene has motivated various research including studies on the perceptions and motivations of fake... | ABC | ABC | BCA | ACB | Selection 4 |
**A**:
We conduct an analysis of the training time for decentRL and AliNet with varying hidden-sizes on a V100 GPU, as detailed in Table 12**B**: The two methods exhibit comparable running times per epoch. AliNet runs marginally faster than decentRL with smaller hidden sizes, but the total training time of decentRL is... | ACB | BAC | BCA | ABC | Selection 1 |
**A**: Normalization methods**B**: We normalize the intrinsic reward and advantage function in training for more stable performance**C**: Since the reward generated by the environment are typically non-stationary, such normalization is useful for a smooth and stable update of the value function. In practice, we normali... | ACB | ABC | ACB | BCA | Selection 2 |
**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**: That is: For given arbitrary nodes P𝑃Pitalic_P, determine the polynomial space ΠΠ\Piroman_Π such that
P𝑃Pitalic_P is unisolvent with respect to ΠΠ\Piroman_Π**C**: We complement th... | CBA | BCA | BAC | BCA | Selection 1 |
**A**: On the other hand, if the unconstrained nuisance variables have enough capacity, the model can use them to achieve a high quality reconstruction while ignoring the latent variables related to the disentangled factors. This phenomena is sometimes called the "shortcut problem" and has been discussed in previous wo... | ABC | BCA | CAB | BAC | Selection 3 |
**A**: We will look at the inputs through 18 test cases to see if the circuit is acceptable**B**: Next, it verifies with DFS that the output is possible for the actual pin connection state**C**: As mentioned above, the search is carried out and the results are expressed by the unique number of each vertex. The result i... | CAB | CBA | BAC | ABC | Selection 4 |
**A**: Some well-studied families of polynomials include the Dickson polynomials and reverse Dickson polynomials, to name a few**B**: Conditions for such families of maps to define a permutation of the field 𝔽𝔽\mathbb{F}blackboard_F are well studied and established for special classes like Dickson polynomials [20], l... | ACB | CAB | BCA | ACB | Selection 3 |
**A**: Note that we are primarily interested in the extent to which differences between the meta-learners are moderated by the experimental factors of sample size, view size, number of views, and correlation structure**B**: The values of partial η2superscript𝜂2\eta^{2}italic_η start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRI... | CAB | CAB | ACB | BAC | Selection 4 |
**A**: Various anomaly detection methods have been developed to leverage the distinctive characteristics of anomalies that deviate from the norm in some manner**B**: In this section, we provide a concise overview of the proximity-based approach and then delve into existing dependency-based methods. Subsequently, we bri... | BCA | BCA | ACB | BCA | Selection 3 |
**A**: We focus on performance comparison for varying values of parameter κ𝜅\kappaitalic_κ, and show that our algorithm has a consistently superior performance for different κ𝜅\kappaitalic_κ values in Figure 2**B**:
In this section we compare the empirical performance of our proposed algorithm CB-MNL with the previo... | BAC | BCA | CBA | CBA | Selection 1 |
**A**: Clip O and Clip U**B**: In Table 5, we compare the performance when generating predictions only from Clip O, only from Clip U, and from both with the same well-trained VSGN model**C**: We can see that the two clips still result in different performance even after their features are aggregated throughout the netw... | BCA | ACB | CBA | ABC | Selection 4 |
**A**: Exploring the impact of the addition and removal of algorithms and models in a majority-voting ensemble from different perspectives and tracking the crossover and mutation process enables users to be sure how to proceed with the selection of hyperparameters for a single model or complex ensembles that require a ... | CBA | BCA | BAC | BAC | Selection 2 |
**A**: However, these algorithms may become computationally infeasible when dealing with swarms that comprise hundreds to thousands of agents.**B**: Additionally, [35] offers an overview of existing swarm robotic applications.
For swarm guidance purposes, certain deterministic algorithms have been developed in [36, 37,... | BAC | BCA | BAC | CBA | Selection 4 |
**A**: Our algorithm is efficient, straightforward to implement, and montonically increases the objective function. Experimentally we have demonstrated that our method outperforms recent state-of-the-art techniques in terms of matching quality, while producing cycle-consistent results and being efficient.**B**: By doin... | BAC | CBA | ACB | ABC | Selection 2 |
**A**: Note that it is an implementation of Theorem 6 with very small changes**B**: We present the algorithm RecognizePG**C**: W.l.o.g., we assume that G𝐺Gitalic_G is connected, indeed a graph G𝐺Gitalic_G is a path graph if and only if all its connected components are path graphs. Moreover, we can obtain the clique p... | CAB | BCA | BAC | BCA | Selection 3 |
**A**: The smallest group with only 2 nodes in UKfaculty data is removed. Table 1 presents some basic information about the four datasets.
**B**: For the Polbooks data, nodes labeled as “neutral” are removed**C**: Before comparing these methods, we take some preprocessing to remove nodes that may have mixed memberships... | CBA | ABC | ACB | ABC | Selection 1 |
**A**: (2018); Cheng and Bartlett (2018); Chatterji et al. (2018); Wibisono (2018); Bernton (2018); Dalalyan and Karagulyan (2019); Baker et al. (2019); Ma et al. (2019a, b); Mou et al. (2019); Vempala and Wibisono (2019); Salim et al. (2019); Durmus et al. (2019); Wibisono (2019) and the references therein.
Among thes... | CBA | ACB | ACB | CAB | Selection 1 |
**A**: However,
as the number of agents increases, joint optimization usually leads to dimensional explosion, which has inhibited the widespread adoption of such methods to a large-scale traffic signal control. To overcome the difficulty, another type of methods are implemented in a decentralized manner. For example, t... | ABC | ACB | BCA | ACB | Selection 3 |
**A**: We will now use Lemma 2 to prove a more general result that incorporates the prediction error into the analysis**B**: Specifically, we will argue that in the presence of prediction error, the cost of ProfilePacking may be affected in two ways: The number of bins in a single profile of ProfilePacking may increase... | ACB | CAB | BAC | CAB | Selection 1 |
**A**: CD is multiplied by 104superscript10410^{4}10 start_POSTSUPERSCRIPT 4 end_POSTSUPERSCRIPT, and EMD is multiplied by 102superscript10210^{2}10 start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT. (HC) denotes the HyperCloud autoencoder in LoCondA, and (HF) - the HyperFlow autoencoder.
**B**: The best results are highligh... | CAB | CBA | ABC | CAB | Selection 2 |
**A**: Instead of the smooth convex-concave saddle-point problem we can consider general sum-type saddle-point problems with common variables in more general form. For each group of common variable, we introduce corresponding communication network which includes the nodes correspond to the terms contain this variable. ... | CAB | BAC | CBA | BCA | Selection 1 |
**A**: In [6] the authors characterize them in terms of their corresponding cycle matrices and present a Venn diagram that shows their inclusion relations**B**:
Different classes of cycle bases can be considered**C**: Among these classes we can find the strictly fundamental class. | ACB | CAB | BAC | CBA | Selection 3 |
**A**: 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**B**: of Patáková [35, Theorem 2.3] into:
**C**: One immediate application of Theorem 1.2 is the reduction of fra... | CBA | ABC | ABC | BCA | Selection 4 |
**A**: G4: Generation of new features and comparison with the original features.
With the same statistical evidence as defined in G3, users should get visually informed about strongly correlated features that perform the same for each class**B**: Finally, the tool should let users select the proper mathematical operati... | BAC | ACB | CAB | CAB | Selection 2 |
**A**: 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]. High-precision trajectories or set points can be generated prior to the actual machining process following... | CAB | ABC | CAB | BAC | Selection 2 |
**A**: To confirm this, we add CoordConv layers [42] before and after the maxpooling layer in CNN to enable usage of position information. This resulted in methods exploiting digit position too, showing larger MMD values of 11.1%-25.6% as compared to the 2.2%-8.7% without the CoordConv layers. Such inductive biases aff... | CAB | BAC | ACB | ACB | Selection 1 |
**A**: They usually require few-shot annotated samples for calibration.
Park et al. propose a meta learning-based calibration approach [47]**B**: Meta learning and metric learning show great potentials in personalized gaze estimation**C**: They train a highly adaptable gaze estimation network through meta learning. | ABC | BAC | CAB | CAB | Selection 2 |
**A**: Note that this comparison is performed using the same pre-trained models (VGG-16 and AlexNet) on the RMFRD dataset**B**:
The comparison of the computation times between the proposed method and Almabdy et al.’s method almabdy2019deep shows that the use of the BoF paradigm decreases the time required to extract ... | ACB | BCA | BAC | ABC | Selection 3 |
**A**:
First, we define head and tail observations on streams of arbitrary depth**B**: Since they are not recursive, we do not bother tracking the size superscript of the typing judgment, since they can be inlined**C**: Moreover, we take the liberty to nest values (boxed and highlighted yellow), which can be expanded ... | BAC | CBA | ABC | BCA | Selection 3 |
**A**: We regard this as the trade-off between security and cost. In actual use, the two proposed schemes can be selected according to different security requirements. The flexibility of choice in cloud-side efficiency also constitutes one of the prominent advantages of our work.
**B**: Comparing to FairCMS-I, it is ea... | ABC | CBA | ACB | CAB | Selection 2 |
**A**: This provides some rationales for the model prediction.**B**: This proves that our model can indeed select meaningful feature combination and model feature interactions of increasing orders with multiple layers in most cases, rather than select the redundant feature combinations of same feature fields.
We can al... | ACB | ABC | CAB | ABC | Selection 3 |
**A**: [2022] is in essence the Frank-Wolfe algorithm with a modified version of the backtracking line search of Pedregosa et al**B**: [2020]. In the next section, we provide improved convergence guarantees for various cases of interest for this algorithm, which we refer to as the Frank-Wolfe algorithm with Backtrack (... | ABC | CAB | CAB | BCA | Selection 4 |
**A**: 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). Each of these routines is performed in a separate pass over the ed... | BCA | CAB | BAC | CBA | Selection 1 |
**A**: In the second part of this paper, we propose a broadcast-like CPP algorithm (B-CPP) that allows for asynchronous updates of the agents: at every iteration of the algorithm, only a subset of the agents wake up to perform prescribed updates**B**: We show that B-CPP also achieves linear convergence for minimizing s... | CBA | CBA | CAB | ACB | Selection 4 |
**A**: Recently, significant attention was devoted to saddle problems in machine learning**B**: For example, Generative Adversarial Networks (GANs) are written as a min-max problem [12]. In addition, there are many popular examples: robust models with adversarial noise [13],**C**: One can note a branch of recent work d... | CAB | CBA | CBA | BCA | Selection 4 |
**A**: We prove that the resulting algorithm converges to a normal form (C)CE in the extensive form game. In Section 6 we conduct an empirical study and show convergence rates and social welfare across a variety of games including n-player, general-sum, and common-payoff games.**B**: In Section 5 we propose a novel tra... | CBA | ABC | BCA | CAB | Selection 1 |
**A**:
We prove these theorems via a new notion, pairwise concentration (PC) (Definition 4.2), which captures the extent to which replacing one dataset by another would be “noticeable,” given a particular query-response sequence**B**: We then build a composition toolkit (Theorem 4.4) that allows us to track PC losses ... | CBA | BAC | ACB | ABC | Selection 3 |
**A**:
Our algorithmic results are based on a combination of graph reduction and color coding [6] (more precisely, its derandomization via the notion of universal sets)**B**: After such reduction steps, the size of the entire structure we are trying to find can be bounded in terms of the parameter k𝑘kitalic_k. We the... | BAC | ACB | ABC | BCA | Selection 2 |
**A**: Some works [203, 92] construct rendered datasets with paired data by inserting a virtual object into 3D scene and generating shadow for this object with rendering technique.**B**: Nonetheless, it is very difficult to obtain paired data, i.e., a composite image without foreground shadow and a ground-truth image w... | CBA | CAB | CAB | CAB | Selection 1 |
**A**: This can be attributed to the fact that LPA optimizes the expected long-term revenues at each dispatching round, while LLD only focuses on the immediate reward**B**:
Our experimental results demonstrate that LPA outperforms LLD in most cases**C**: As a result, LPA is better suited for maximizing the total reven... | BAC | CBA | BCA | CAB | Selection 1 |
**A**: Recently some advances were made in which β𝛽\betaitalic_β-calibration pmlr-v54-kull17a was generalized to regression problems using a Gaussian process approach pmlr-v97-song19a . However, as mentioned before, a Gaussian process does not have a favorable scaling behaviour and also in this case certain approxima... | CAB | BCA | CBA | CBA | Selection 2 |
**A**: Their CNN learns to predict the probability that each note belongs to the melody line**B**: Finally, the Bellman-Ford algorithm is adopted to pick a strictly monophonic melody line.
In contrast, we do not have postprocessing steps such as thresholding or clustering in our BERT-based model and the RNN baseline.**... | ACB | BAC | CBA | CBA | Selection 1 |
**A**: Next, let us count the total number of jumps necessary for finding central vertices over all loops in Algorithm 1**B**: 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.
**C**: As it was stated... | CAB | BCA | BAC | ACB | Selection 4 |
**A**: However, in this paper, we consider an intelligent task at the receiver to recover the text information of the input speech signals**B**: Particularly, we propose a DL-enabled semantic communication system for speech recognition, named DeepSC-SR, by learning and extracting the text-related semantic features from... | CAB | BCA | BAC | ABC | Selection 2 |
**A**:
Comparison with fully supervised methods: We compare our weakly supervised method with some fully supervised state-of-the-art methods[23, 24, 25, 26, 2, 26, 4] on the public dataset S3DIS Area-5**B**: Our method produces even slightly higher results using 10% label than trained under full supervision by 0.1%. W... | ACB | CAB | BAC | CBA | Selection 1 |
**A**: Table 4 shows more depth estimation results on KITTI val set via comparing the enhanced baseline and our method. Specifically, we evaluate the depth estimation by computing Scale Invariant Logarithmic (SILog) error, squared Relative (sqRel) error, absolute Relative (absRel) error, and Root Mean Squared Error of ... | ACB | BCA | ABC | BCA | Selection 1 |
**A**: Existing bottom-up methods (e.g., [22, 7, 23, 24, 25, 13, 26, 27],) with CNN, RNN and some pre-defined heuristic rules have considered visual similarity features, sequential features and geometric features in order to connect text segments.**B**: \add
To understand the limitations of bottom-up methods, we need t... | BAC | CAB | BCA | BAC | Selection 2 |
**A**: The average number of IP records is 100 for each individual IP address. Parameter k𝑘kitalic_k represents the number of frequently occurring IP addresses. The statistics of these datasets are summarized in Table 2.**B**:
In this section, we evaluate the performance of the proposed methods 111https://github.com/... | ACB | BCA | CAB | ACB | Selection 3 |
**A**: Usually, D𝐷Ditalic_D is assumed to be a symmetric and semi-positive definite**B**: KKT system or saddle point system**C**: In this paper, we only make some assumptions
that can guarantee the invertibility of 𝒜𝒜\mathcal{A}caligraphic_A. We assume that A𝐴Aitalic_A and the Schur complement Schur(𝒜)Schur𝒜\mbo... | CBA | CAB | CBA | BAC | Selection 4 |
**A**: However, in cases when the labels are sensitive and sharing the labels for a sample ID across silos is not feasible, the label information for a sample ID may only be present in a client in one silo. In this case, we could modify our algorithm in the following way, similar to (Liu et al., 2020a): the clients in ... | BAC | ACB | CAB | CAB | Selection 2 |
**A**:
Within the framework of tensor-tensor multiplication (3) proposed and investigated by Kilmer and Martin Kilmer2011 , T-eigenvalues and T-eigenvectors have garnered significant attention from researchers. They offer a novel perspective to characterize the properties of the widely employed tensor-tensor multiplic... | BAC | CBA | ACB | CAB | Selection 3 |
**A**:
On Structure Priors**B**: To highlight the structure priors, we build a single-stream network as baseline, which fills missing regions by solely modeling texture features, and the discriminator is single-stream accordingly**C**: As shown in Figure 7 (b), the baseline method does not well deal with complex struc... | BCA | ABC | BCA | BAC | Selection 2 |
**A**: In a binary erasure channel (BEC), a binary symbol is either received correctly or totally erased with probability ε𝜀\varepsilonitalic_ε**B**: 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, an... | BAC | ACB | CAB | BAC | Selection 2 |
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