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**A**: We present them as subroutines here to improve the readability of Algorithm 3. However, we assume Algorithms 4–7 have access to the variables of Algorithm 3 in an implementation and that the Algorithms 4–7 can also use and modify variables of Algorithm 3. The variables of Algorithm 3 modified and used by Algorit... | ACB | CAB | BCA | CBA | Selection 2 |
**A**: Solving (25) on the other hand involves computing the hℎhitalic_h-dependent, global operator P𝑃Pitalic_P, leading to a dense matrix in (25)**B**: 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**C**: From now ... | ACB | CAB | BAC | ABC | Selection 3 |
**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 | ACB | Selection 3 |
**A**: The first terror-indicating “news” –The gunman shouted ‘Allahu Akbar’– was widely disseminated on Twitter right after the incident by an unverified account**B**: The city police had to warn the population to refrain from spreading related news on Twitter as it was getting out of control: “Rumors are wildfires th... | ACB | ABC | ABC | BAC | Selection 4 |
**A**: However, if we initialize with η<1/ℒ(𝐰(0))𝜂1ℒ𝐰0\eta<1/\mathcal{L}(\mathbf{w}(0))italic_η < 1 / caligraphic_L ( bold_w ( 0 ) ) then it is straightforward to show the gradient descent iterates maintain bounded local smoothness**B**: Assumption 1 includes many common loss functions, including the logistic, exp... | CBA | BAC | BCA | CAB | Selection 2 |
**A**: The tweet is ”Sadly, i think there’s something terrible happening in #Munich #Munchen. Another Active Shooter in a mall. #SMH”.**B**:
At 18:22 CEST, the first tweet was posted**C**: There might be some certain delay, as we retrieve only tweets in English and the very first tweets were probably in German | ACB | CAB | CBA | BAC | Selection 2 |
**A**: We further investigate the identification of event time, that is learned on top of the event-type classification**B**: For the gold labels, we gather from the studied times with regards to the event times that is previously mentioned**C**: We compare the result of the cascaded model with non-cascaded logistic re... | CAB | CBA | ACB | ABC | Selection 4 |
**A**: The insulin intakes tend to be more in the evening, when basal insulin is used by most of the patients**B**: The only difference happens to patient 10 and 12 whose intakes are earlier at day.
Further, patient 12 takse approx**C**: 3 times the average insulin dose of others in the morning. | ABC | CAB | BAC | CAB | Selection 1 |
**A**:
Table 1: Quantitative results of our model for the MIT300 test set in the context of prior work**B**: The first line separates deep learning approaches with architectures pre-trained on image classification (the superscript ††{}^{\dagger}start_FLOATSUPERSCRIPT † end_FLOATSUPERSCRIPT represents models with a VGG... | ABC | ACB | ACB | BCA | Selection 1 |
**A**: These strategies are – except for LeftRightLeftRight\operatorname{\textsf{LeftRight}}LRstrategy – nondeterministic, since there are in general several valid choices of the next symbol to mark**B**: However, we will show poor performances for these strategies independent of the nondeterministic choices (i. e., th... | ABC | BCA | CBA | BCA | Selection 1 |
**A**: Also, since our model is differentiable, the additional information contained in its gradients could be incorporated into the reinforcement learning process. Finally, the representation learned by the predictive model is likely be more meaningful by itself than the raw pixel observations from the environment. In... | CAB | CBA | BCA | ABC | Selection 1 |
**A**: 2.2, are employed. In order to facilitate motion control in both locomotion modes, all the necessary kinematics and dynamics calculations are carried out within the CoppeliaSim simulation environment. This includes computing torques and angular velocities for each joint. The simulation outputs, along with these ... | CAB | BAC | ABC | ABC | Selection 1 |
**A**: In Section 6, we study the power of randomization in online computation with untrusted advice**B**:
All the above results pertain to deterministic online algorithms**C**: First, we show that the randomized algorithm of Purohit et al. [29] for the ski rental problem Pareto-dominates any deterministic algorithm, ... | BAC | BCA | CAB | ABC | Selection 1 |
**A**: Additionally, terms having a document frequency lower than 20 were ignored. Finally, classifiers were coded using their corresponding sklearn built-in classes, e.g. LogisticRegression, KNeighborsClassifier, MultinomialNB, etc.
**B**: Thus, all these other models were also implemented in Python 2.7, using the skl... | BAC | CAB | ABC | BAC | Selection 2 |
**A**: More details about the convergence performance of GMC are provided in Section 5.**B**: Note that we impose a constraint on the momentum coefficient β𝛽\betaitalic_β during the theoretical proof**C**: But in practice, even when the constraint is relaxed, e.g., β=0.9𝛽0.9\beta=0.9italic_β = 0.9,
GMC still converge... | ACB | CAB | CBA | BCA | Selection 2 |
**A**: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization**B**: operation.**C**:
, where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks | CBA | ACB | CBA | BCA | Selection 4 |
**A**: Underlay Device-to-Device (D2D) communication is considered as one of the crucial technologies for cellular spectrum reuse for user devices in communication networks [19]. The advantage of D2D communication that allows end users to operate on licensed channels through power control sheds light on how interferenc... | ABC | ABC | CAB | CBA | Selection 3 |
**A**: italic_e **B**: the boundary condition (∇∥ψ)|Γ=0evaluated-atsubscript∇parallel-to𝜓Γ0\left(\nabla_{\parallel}\psi\right)|_{\Gamma}=0( ∇ start_POSTSUBSCRIPT ∥ end_POSTSUBSCRIPT italic_ψ ) | start_POSTSUBSCRIPT roman_Γ end_POSTSUBSCRIPT = 0
(i.e.,formulae-sequence𝑖𝑒i.e.,italic_i **C**: , ψ|Γ=Cevaluated-at𝜓Γ𝐶\p... | CBA | BAC | CAB | ABC | Selection 2 |
**A**: Many of the proposed extensions focus on minimizing the variance that comes from AGE by finding methods to optimize the learning trajectory or from TAE by using methods like averaging to exact DQN parameters. Dropout methods have the ability to assemble these two solutions which minimize different source of vari... | ACB | BAC | CAB | CBA | Selection 3 |
**A**:
Figure 13: Comparison of cross entropy and Dice losses for segmenting small and large objects**B**: The red pixels show the ground truth and the predicted foregrounds in the left and right columns respectively**C**: The striped and the pink pixels indicate false negative and false positive, respectively. For th... | BCA | ABC | BAC | BCA | Selection 2 |
**A**: SVM: Support vector machine (Chang & Lin, 2011) is a popular classifier that tries to find the best hyperplane that maximizes the margin between the classes**B**: (2014), the best performance is achieved with a radial basis function kernel.
**C**: As evaluated by Fernández-Delgado et al | ABC | ACB | CAB | ABC | Selection 2 |
**A**: To answer this question, we propose the first policy optimization algorithm that incorporates exploration in a principled manner. In detail, we develop an Optimistic variant of the PPO algorithm, namely OPPO**B**: As is shown subsequently, solving such a subproblem corresponds to one iteration of infinite-dimens... | ABC | ACB | BAC | ABC | Selection 2 |
**A**: is difficult as they cannot be directly optimized using gradient-based methods.**B**:
However, training such discrete-valued DNNs555Due to finite precision of computer arithmetic, in fact any DNN is discrete-valued**C**: However, we use this term here to emphasize the extremely small number of values | ABC | ABC | ABC | CAB | Selection 4 |
**A**: For example, Carlsson and de Silva [17] applied Vietoris-Rips persistent homology to topological estimation from point cloud data, and Ghrist and de Silva applied it to sensor networks [28]. Its efficient computation has been addressed by Bauer in [11]. A more detailed historical survey and review of general ide... | CBA | ABC | BCA | CAB | Selection 1 |
**A**: The difference line plot (d), on the other hand, builds on the standard plot by highlighting the differences between the selection and the global average, shown as positive and negative values around the 0 value of the y-axis.
It provides a clearer overall picture of the difference in preservation among all the ... | CBA | CAB | BAC | ACB | Selection 4 |
**A**: To encourage better comparison methodologies, the most promising avenues are the use of existing benchmarks and also the creation of new ones based on real-world problems**B**: Moreover, better comparison methodologies, including more attention to scalability and new statistical testing approaches such as the us... | BAC | CAB | CBA | BCA | Selection 4 |
**A**: (1) Via extending the generative graph models into general type data, GAE is naturally employed as the basic representation learning model and weighted graphs can be further applied to GAE as well**B**: We analyze the degeneration theoretically and experimentally to understand the phenomenon. We further propose ... | ABC | ABC | ACB | BAC | Selection 3 |
**A**: If the ICMP packets are not blocked (but were blocked when the packets were sent from a spoofed IP address) then the network does not block ICMP packets and does enforce IP spoofing filtering. On the other hand if the packets are blocked then one cannot determine if the blocking is done because of ICMP or becaus... | CBA | BCA | BCA | ACB | Selection 1 |
**A**: This design introduces variation in training inputs, which makes it harder to learn consistent context patterns. For this task, semisupervised learning techniques, such as self-labeled samples, may help**B**: If the context layer can process unlabeled data, then it is no longer necessary to include every class i... | CAB | BCA | CAB | CAB | Selection 2 |
**A**: While these constructions and the involved proofs are generally deemed quite complicated, the situation for semigroups turns out to be much simpler. While it is known that the free semigroup of rank one is not an automaton semigroup [4, Proposition 4.3], the free semigroups of higher rank can be generated by an ... | BCA | ACB | ABC | CBA | Selection 1 |
**A**: (2019), we train HINT on the subset with human-based attention maps Das et al**B**: Following Selvaraju et al**C**: (2017), which are available for 9% of the VQA-CPv2 train and test sets. The same subset is used for VQAv2 too. The learning rate is set to 2×10−52superscript1052\times 10^{-5}2 × 10 start_POSTSUPER... | ABC | BAC | CAB | ABC | Selection 2 |
**A**: While Wilson et al**B**: (2016) followed a bottom-up approach and identified different categories from analysis of data practices in privacy policies, we followed a top-down approach and applied topic modelling to the corpus in order to extract common themes for paragraphs. The categories identified in the OPP-1... | CBA | BAC | BCA | CBA | Selection 3 |
**A**: EnsembleLens [65] is a VA system that focuses on the identification of the best combination of models by visualizing their correlation**B**: Then, the results are combined and ranked based on the performance outcomes for anomalous cases.
In contrast, our work is not limited to the anomaly detection task, and it ... | BAC | ACB | BCA | BCA | Selection 2 |
**A**: They are datasets for 5-way 5-shot classification, which means 5 classes are randomly sampled from the full dataset for each task, and each class has 5 samples**B**: FewRel is a relation classification dataset with 65/5/10 tasks for meta-training/meta-validation/meta-testing.**C**:
In Experiment I: Text Classif... | CAB | ACB | CAB | BCA | Selection 4 |
**A**: However, there are critical challenges to achieve reliable mmWave communications for UAV networks. Specifically, a UAV maintains three-dimensional or full-spatial mobility with very high dynamic, and thus the angle-of-arrival (AOA) of communication signal always varies over time in all directions. To this end, a... | CAB | ABC | ABC | BAC | Selection 4 |
**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... | ACB | CBA | CAB | BAC | Selection 4 |
**A**: In this section, we extend our analysis of TD to Q-learning and policy gradient**B**: In §6.1, we introduce Q-learning and its mean-field limit**C**: In §6.2, we establish the global optimality and convergence of Q-learning. In §6.3, we further extend our analysis to soft Q-learning, which is equivalent to polic... | CAB | BAC | CAB | ABC | Selection 4 |
**A**: Given that the depth-wise LSTM unit only takes one input, we introduce a merging layer to merge the outputs of these two sub-layers into one as the input to the LSTM unit**B**: The architecture is shown in Figure 3 (a).**C**:
Different from encoder layers, decoder layers involve two multi-head attention sub-lay... | CAB | ABC | ACB | BCA | Selection 4 |
**A**: ∃ italic_y **B**: x)\wedge\neg(x=y)italic_ψ start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ≜ ∃ italic_x **C**: italic_ε start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ( italic_x ) ∧ italic_ε start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ( italic_x ) ∧ ¬ ( italic_x = italic_y ) for i∈I𝑖𝐼i\in Iitalic_i ∈ italic... | CAB | CAB | ACB | BAC | Selection 4 |
**A**: To bridge the gap between image feature and calibration objective, we present a novel intermediate representation, i.e., ordinal distortion, which displays a learning-friendly attribute for learning models. For an intuitive and comprehensive analysis, we compare these two representations from the following three... | CBA | BCA | CAB | CBA | Selection 3 |
**A**: Similar to the results of image classification, SNGM outperforms LARS for different batch sizes.
**B**: Table 6 shows the test perplexity of the three methods with different batch sizes**C**: We can observe that for small batch size, SNGM achieves test perplexity comparable to that of MSGD, and for large batch s... | BAC | CAB | ABC | CBA | Selection 2 |
**A**: This new algorithm is intricate and may be of interest on its own.**B**: In Section 5, we describe a 9-approximation algorithm for an inhomogeneous MatSup problem, which is an extension of results in [13] and [3] (specifically, our method in Algorithm 6 involves an iterative rounding approach similar to that of ... | ABC | BCA | CBA | BAC | Selection 3 |
**A**:
Motivated by distributed statistical learning over uncertain communication networks, we study the distributed stochastic convex optimization by networked local optimizers to cooperatively minimize a sum of local convex cost functions. The network is modeled by a sequence of time-varying random digraphs which ma... | BCA | ABC | CBA | CBA | Selection 2 |
**A**: The major research of privacy preservation focuses on preventing various disclosures and studying the trade-off between privacy protection and information preservation [20, 32, 21, 17, 11]**B**: The generalization technique has been well-studied by proposing numerous algorithms which can be divided into three sc... | BAC | BAC | CAB | ABC | Selection 4 |
**A**: As shown in Figure 2, we compare HTC, SOLOv2 and PointRend by visualizing their predictions**B**: It can be seen that PointRend generates much finer and smoother segmentation boundaries than HTC and SOLOv2, it also handles overlapped instances gradely (see top-left corner in Figure 2)**C**: Meanwhile, PointRend ... | ACB | ACB | ABC | BCA | Selection 3 |
**A**: [KKLMS] establishes a weaker version of the conjecture**B**: For the significance of this conjecture we refer to the original paper [FK], and to Kalai’s blog [K] (embedded in Tao’s blog) which reports on all significant results concerning the conjecture**C**: Its introduction is also a good source of information... | BAC | CAB | CBA | BCA | Selection 1 |
**A**: The definition of total variation B𝐵Bitalic_B is related to the misspecification error defined by Jin et al. (2020)**B**: One can apply the Cauchy-Schwarz inequality to show that our total variation bound implies that misspecification in Eq. (4) of Jin et al. is also bounded (but not vice versa)**C**: However, ... | BCA | CAB | ACB | ABC | Selection 4 |
**A**: The presence of fake news has become more prolific on the Internet due to the ease of production and dissemination of information online (Shu et al., 2017)**B**: The usage of fake news ranges from self-serving purposes like clickbait for moneymaking (Geçkil et al., 2018) to agendas on a national scale like polit... | ACB | ABC | BCA | ACB | Selection 3 |
**A**: Typically, an entity with a higher degree indicates that it has more neighboring entities**B**: Consequently, the computation of attention scores to aggregate these neighbors becomes crucial.
**C**: We conduct experiments to investigate the performance gain concerning entity degrees | BAC | BCA | CBA | CBA | Selection 2 |
**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... | CBA | BAC | CBA | ABC | Selection 4 |
**A**: However, Floater-Hormann becomes indistinguishable from 5thsuperscript5𝑡ℎ5^{th}5 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT-order splines.
Further, when considering the amount of coefficients/nodes required to determine the interpolant, plotted in the right panel (with logarithmic scales on bo... | CAB | CBA | CBA | BCA | 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... | BCA | CAB | ABC | ACB | Selection 1 |
**A**: We decided to verify that the structural computer theory presented so far is actually working without the cost of circuit building, to simulate the connection of complex Circuits rather than just Gate Circuit, and to set up Metric for experiments that can test structural computers for logical errors and error.
*... | ACB | CBA | ABC | BAC | Selection 2 |
**A**: 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], linearized polynomials [21] and few other specific forms [13, 14] to name a few.
**B**: There has been extensive study about a f... | ACB | BCA | ABC | CAB | Selection 4 |
**A**: The interpolating predictor shows behavior that is completely different from the other meta-learners. Whereas for the other meta-learners the TPR increases as sample size increases, the TPR of the interpolating predictor actually decreases in some cases. Although it appears to have the highest TPR in some condit... | ACB | CAB | ACB | BCA | Selection 2 |
**A**: Upon examining the violin plots of the five techniques in Figure 5, we observe more consistent shapes compared to the violin plots in Figures 3 and 4. Most dots in Figure 5 are located in the upper part, a few dots are located in the middle part and low parts**B**: Each dot represents the performance of a combin... | CAB | BAC | BAC | ABC | Selection 4 |
**A**: This highlights the primary contribution of our theoretical analysis. Refer to A.8 for additional empirical analysis.**B**: 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 val... | ABC | ABC | CBA | CAB | Selection 3 |
**A**: From Table 3 and 4, we can see that xGPN obviously improves the performance of short actions as well as the overall performance**B**: On the one hand, xGPN utilizes long-range correlations in multi-level features and benefits actions of various lengths. On the other hand, xGPN enables exploitation of cross-scale... | BAC | CAB | BCA | CAB | Selection 3 |
**A**: Visualizations arranged into dashboard-styled interfaces are the preferred norm for managing ML experiments and their associated models [SKJ∗17, TMB∗18, WRW∗20, WWO∗20].
Automated approaches exclude the user from exploring and refining the hyperparameter search, and the visual representation of automation happen... | ACB | CAB | BAC | ACB | Selection 2 |
**A**: This, in turn, ensures the exponential convergence of the Markov chain to the desired steady-state.
Subsequently, the DSMC algorithm’s performance is demonstrated on the probabilistic swarm guidance problem, with the objective of controlling the spatial distribution of swarm agents. Through simulations, it is sh... | CAB | BCA | ABC | CBA | Selection 1 |
**A**: By doing so, we obtain the initial U𝑈Uitalic_U and Q𝑄Qitalic_Q. We refer to this method of synchronising the ZoomOut results as ZoomOut+Sync, which directly serves as initialisation for HiPPI and our method**B**: Throughout this section we also report results of the initialisation methods ZoomOut and ZoomOut+S... | CAB | ACB | BCA | BAC | Selection 3 |
**A**: For example, every cycle has no clique separator, and the butterfly/hourglass graph has two cliques and it is an atom. In [18] it is proved that an atom is a path graph and/or a directed path graph if and only if it is a chordal graph; moreover, every chordal graph that is an atom has at most two cliques.**B**: ... | ACB | CBA | BAC | CAB | Selection 2 |
**A**:
Numerical results of these two sub-experiments are shown in panels (a) and (b) of Figure 1, respectively**B**: From the results in subfigure 1(a), it can be found that Mixed-SLIM performs similarly to Mixed-SCORE while both two methods perform better than OCCAM and GeoNMF under the MMSB setting**C**: Subfigure ... | ABC | ACB | CBA | CAB | Selection 1 |
**A**: (2016); Chen et al. (2016); Dalalyan (2017); Chen et al. (2017); Raginsky et al. (2017); Brosse et al. (2018); Xu et al**B**: See, e.g., Welling and Teh (2011); Chen et al. (2014); Ma et al. (2015); Chen et al. (2015); Dubey et al. (2016); Vollmer et al**C**: (2018); Cheng and Bartlett (2018); Chatterji et al. (... | BAC | BCA | BCA | CBA | Selection 1 |
**A**: The arrival rate changes every 10 minutes, which is used to simulate the uneven traffic flow distribution in the real world, the details of the vehicle arrival rate and cumulative traffic flow are shown in Fig. 8.**B**:
Mixedl**C**: The mixedl is a mixed low traffic flow with a total flow of 2550 in one hour, t... | BCA | ABC | ABC | CAB | Selection 4 |
**A**: Similar types of sampling-based competitive analysis have recently attracted attention in the context of other online problems such as ski rental and prophet inequalities (?), matching (?), and network optimization problems (?).
**B**: More precisely, consider the setting in which the online algorithm is allowed... | ACB | BCA | CBA | CAB | Selection 3 |
**A**: Practically speaking, our approach transforms the embedding of point cloud obtained from the base model to parametrize the bijective function represented by the MLP network**B**: This function aims to find a mapping between a canonical 2D patch to the 3D patch on the surface of the target mesh**C**: We condition... | BAC | CBA | ABC | ACB | Selection 3 |
**A**: Paper [61] introduced an Extra-gradient algorithm for distributed multi-block SPP with affine constraints**B**: Their method covers the Euclidean case and the algorithm has O(1/N)𝑂1𝑁O(1/N)italic_O ( 1 / italic_N ) convergence rate.
Our paper proposes an algorithm based on adding Lagrangian multipliers to cons... | ABC | CAB | ACB | BAC | Selection 1 |
**A**: This problem was formulated by Stepanec [7] and Zykov [8] for general graphs and by Hubicka and Syslo [9] in the strictly fundamental class context. In more concrete terms this problem is equivalent to finding the cycle basis with the sparsest cycle matrix**B**: In [5] a unified perspective of the problem is pre... | BCA | ABC | ABC | CBA | Selection 1 |
**A**: One immediate application of Theorem 1.2 is the reduction of fractional Helly numbers**B**: of Patáková [35, Theorem 2.3] into:
**C**: 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 setti... | BCA | CBA | CBA | ACB | Selection 4 |
**A**: We believe that four slices are already a good start to explore the vast majority of the data space because users will often focus on particular areas of interest.
The two interactive thresholds are a key component here because they allow users to choose the size of each subspace depending on the problem**B**: A... | CAB | BCA | CBA | ABC | Selection 2 |
**A**: The performance metrics evaluating infinitytracking accuracy and time are summarized in the table for unconstrained and constrained BO.**B**:
Figure 5: Position, velocity, acceleration, and maximal contour error resulting from optimization of the MPC parameters, comparing unconstrained BO optimization (solid li... | BCA | ACB | ACB | CAB | Selection 4 |
**A**: There are two major bias sources: a) class imbalance, with non-blond occurring 5.7 times more than blond hair color, and b) presence of a rare group, i.e., blond male celebrities only account for 0.86% of the training instances.**B**:
The CelebA dataset [43] of celebrity faces is widely used to assess bias miti... | CBA | BAC | CAB | BCA | Selection 3 |
**A**: Meta learning and metric learning show great potentials in personalized gaze estimation**B**: They usually require few-shot annotated samples for calibration.
Park et al. propose a meta learning-based calibration approach [47]**C**: They train a highly adaptable gaze estimation network through meta learning. | BAC | ABC | BCA | BCA | Selection 2 |
**A**: Next, point set matching is carried out to match the obtained features. Finally, the similarity of the two faces is obtained through the distance between these two aligned feature sets. Keypoint based matching method is introduced in Duan et al. duan2018topology **B**: SIFT keypoint descriptor is applied to sele... | CBA | BAC | BCA | ABC | Selection 3 |
**A**: To review SAX, let us make observations about proof-theoretic polarity**B**: In the sequent calculus, inference rules are either invertible—can be applied at any point in the proof search process, like the right rule for implication—or noninvertible, which can only be applied when the sequent “contains enough in... | ABC | ACB | CBA | BCA | Selection 1 |
**A**: Rial et al**B**: [25] designed an innovative user-side AFP scheme based on the symmetric Chameleon encryption technique, which achieves significant gains in owner-side computing and communication efficiency.**C**: [13] proposed a provably secure anonymous AFP scheme based on the ideal-world/real-world paradigm.
... | CAB | ABC | ABC | ACB | Selection 4 |
**A**: Setting a single threshold on all the nodes will lead to the situation that the numbers of nodes’ neighbors vary a lot. Some nodes will have barely any adjacent nodes after cutting off, while some may still have many.**B**: But the performance is not as good as using a fixed-degree graph.
This is reasonable as t... | ABC | CBA | BCA | BCA | Selection 2 |
**A**: [2019] but already implicitly used earlier in Lacoste-Julien & Jaggi [2015] as:
**B**: In order to do so, we recall a quantity formally defined in Kerdreux et al**C**: We can make use of the proof of convergence in primal gap to prove linear convergence in Frank-Wolfe gap | CBA | BAC | CAB | ACB | Selection 1 |
**A**: The rest of our algorithm is divided into multiples phases. In each phase, we iteratively improve the approximation ratio of our current matching M𝑀Mitalic_M by finding a set of disjoint M𝑀Mitalic_M-augmenting paths (and performing the augmentations accordingly). We stop the algorithm after certain number of p... | BCA | BCA | CAB | CBA | Selection 4 |
**A**:
We propose CPP – a novel decentralized optimization method with communication compression**B**: To the best of our knowledge, CPP is the first method that enjoys linear convergence under such a general setting.**C**: The method works under a general class of compression operators and is shown to achieve linear ... | ABC | CAB | BCA | ACB | Selection 4 |
**A**: It is worth considering the use of the variance reduction technique in accelerated sliding to develop an algorithm that is highly efficient in terms of communication and number of iterations**B**: Possible interesting areas for further research are related to the practical features that arise in the federated le... | BAC | BCA | BCA | CBA | Selection 1 |
**A**: One selection criterion for NEs is maximum entropy Nash equilibrium (MENE) (Balduzzi et al., 2018), however outside of the two-player constant-sum setting, these are generally not easy to compute (Daskalakis et al., 2009)**B**:
This highlights the main drawback of MW(C)CE which does not select for unique soluti... | BCA | BAC | CAB | CAB | Selection 2 |
**A**:
Differential privacy essentially provides the optimal asymptotic generalization guarantees given adaptive queries (Hardt and Ullman, 2014; Steinke and Ullman, 2015)**B**: However, its optimality is for worst-case adaptive queries, and the guarantees that it offers only beat the naive intervention—of splitting a... | CBA | BCA | BAC | ABC | Selection 4 |
**A**: We use reduction steps inspired by the kernelization algorithms [12, 46] for Feedback Vertex Set to bound the size of 𝖺𝗇𝗍𝗅𝖾𝗋𝖺𝗇𝗍𝗅𝖾𝗋\mathsf{antler}sansserif_antler in the size of 𝗁𝖾𝖺𝖽𝗁𝖾𝖺𝖽\mathsf{head}sansserif_head, by analyzing an intermediate structure called feedback vertex cut**B**: After s... | BAC | BCA | CBA | ACB | Selection 2 |
**A**: They train a diffusion model on abundant pairs of foregrounds and backgrounds, so that it can be directly applied to a new pair of foreground and background at test time**B**: In [183, 141], they construct massive training triplets of foregrounds, backgrounds, and ground-truth real images based on large-scale im... | ACB | BCA | CAB | BAC | Selection 2 |
**A**: As a result, LPA is better suited for maximizing the total revenue of the system in the long run, and is expected to compare favorably against LLD.**B**:
Our experimental results demonstrate that LPA outperforms LLD in most cases**C**: This can be attributed to the fact that LPA optimizes the expected long-term... | CAB | BAC | ABC | CBA | Selection 1 |
**A**: Instead of having a neural network that produces a single point prediction, the network also estimates the predictive variance, thereby turning it into a mean-variance estimator khosravi2011comprehensive ; nix1994estimating **B**:
Kendall and Gal introduced a new method to estimate uncertainty using dropout net... | CBA | BAC | CBA | CBA | Selection 2 |
**A**: They showed that either a RoBERTa-based Transformer encoder PTM \parenciteroberta or a GPT2-based Transformer encoder PTM \parencitegpt2 outperform non-pre-trained baselines for a 9-class symbolic-domain composer classification task**B**:
To our best knowledge, the work of \textcitetsai20ismir represents the fi... | CBA | BAC | ACB | ABC | Selection 2 |
**A**: This description draws a comparison e.g**B**: [10] for a survey), where the colors of any two adjacent vertices have to differ by at least k𝑘kitalic_k and the colors of any two vertices within distance 2222 have to be distinct.
**C**: to L(k,1)𝐿𝑘1L(k,1)italic_L ( italic_k , 1 )-labeling problem (see e.g | CBA | BAC | CBA | ACB | Selection 4 |
**A**: Moreover, in order to facilitate DeepSC-SR adapting well over various physical channels, a model with strong robustness to channel variations has been investigated. Simulation results demonstrated that DeepSC-SR outperforms the traditional communication systems, especially in the low SNR regime. Hence, our propo... | BAC | BCA | CAB | BCA | Selection 3 |
**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**: Also, we compare our method under weak supervision against full supervision**C**: Our method produces even slig... | ABC | BAC | CBA | BAC | Selection 1 |
**A**: Our method outperforms the enhanced baseline by large margins on all these evaluation metrics**B**: 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) erro... | BAC | CAB | CBA | ACB | Selection 1 |
**A**: The VGG16 is pre-trained with ImageNet, with FPN adopted for multi-scale feature extraction**B**: Our algorithm is implemented using PyTorch 1.7**C**: We conducted our experiments on an RTX3090 GPU with 24GB memory.
All images used for training and testing are of a single scale. | BCA | CBA | BAC | CAB | Selection 3 |
**A**: Finally, we obtain the first k𝑘kitalic_k most frequently occurring IP addresses in the heap. The complete procedure for finding the first k𝑘kitalic_k of the most frequency IP addresses from host logs is outlined in Algorithm 2.**B**: Then, we continue to perform a round traversal of the memory block to adjust ... | ACB | CBA | BCA | CAB | Selection 2 |
**A**: It is shown that by properly selecting the sign in front of each Schur complement, some preconditioners are positively stable. Numerical experiments based on the Biot model are provided to show that positively stable preconditioners outperform other preconditioners. More clearly, when inexact elliptic approximat... | CAB | CBA | BCA | BCA | Selection 1 |
**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... | BAC | BCA | CBA | BAC | Selection 3 |
**A**: Under the same conditions as defined in Definition 7, if the following equation holds:
**B**: We can further extend the concept of T-eigenvalues into generalized T-eigenvalues, similar to the case of generalized matrix eigenvalues**C**: Let ℬℬ\mathcal{B}caligraphic_B be another tensor with the same size as 𝒜𝒜\... | CBA | BAC | ACB | CAB | Selection 4 |
**A**:
User Study. We further perform subjective user study**B**: 10 volunteers with image processing expertise are involved in this evaluation. They are invited to choose the most realistic image from those inpainted by the proposed method and the representative state-of-the-art approaches**C**: Specifically, each pa... | CAB | ABC | CBA | BAC | Selection 2 |
**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 | CBA | BAC | BCA | Selection 1 |
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