shuffled_text stringlengths 267 3.71k | A stringclasses 6
values | B stringclasses 6
values | C stringclasses 6
values | D stringclasses 6
values | label stringclasses 4
values |
|---|---|---|---|---|---|
**A**: We note that after applying the function SlotUsagePattern, the resulting SLP only required 12121212 memory slots and could be evaluated in the same time as our MSLP**B**: This is due to the fact that SlotUsagePattern was handed a well-designed SLP**C**: When faced with an SLP not designed to be memory efficient,... | CBA | CAB | ABC | CBA | Selection 3 |
**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**: From now on, we concentrate on approximating P𝑃Pitalic_P so that (25) can be accurately and efficiently solved.
**C**: Except for (ii), all steps above above can be ... | ABC | ACB | CBA | BCA | Selection 4 |
**A**: 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, this subroutine computes three angles and selects the smallest to decide how to proceed each time, and due to float is... | BCA | CBA | CAB | ABC | Selection 3 |
**A**: Later the claim of three gunmen also spread quickly and caused public tension. In the end, all three information items were falsified.**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 that are difficult to p... | BCA | BCA | ABC | CAB | Selection 4 |
**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... | CAB | BCA | BAC | ACB | Selection 4 |
**A**: Early in an event, the related tweet volume is scanty and there are no clear propagation pattern yet. For the credibility model we, therefore, leverage the signals derived from tweet contents**B**: Thus, a mechanism for carefully considering the ‘vote’ for individual tweets is required. In this work, we overcome... | ACB | CBA | CBA | BCA | Selection 1 |
**A**: We model 𝖽(e)𝖽𝑒\mathsf{d}(e)sansserif_d ( italic_e ) as the corresponding Wikipedia article text**B**: We use the unigram model with default Dirichlet smoothing.
**C**: Language Model-based, how likely aspects are generated by as stastical LM based on the textual representation of the entity 𝖽(e)𝖽𝑒\maths... | CBA | BCA | BAC | ABC | Selection 2 |
**A**: Only one of the patients suffers from diabetes type 2 and all are in ICT therapy. In terms of time since being diagnosed with diabetes, patients vary from inexperienced (2 years) to very experienced (35 years), with a mean value of 13.9 years.**B**: Body weight, according to BMI, is normal for half of the patien... | BAC | BAC | CBA | ACB | Selection 3 |
**A**: The minimal GPU memory utilization was measured with TensorFlow in megabytes (MB) and included the requirements for initializing a testing session. Finally, we estimated the floating point operations per second (FLOPS) at a scale of 9 orders of magnitude.
**B**: After running each network on 10,000 test set inst... | BAC | CBA | BAC | ACB | Selection 2 |
**A**: In this context, our negative result of Section 5.1 can also be interpreted as a series of unconditional results which state that multiple natural greedy strategies for computing the locality number (and their equivalents for computing the cutwidth) do not provide low-ratio approximations of MinLoc (or MinCutwid... | BCA | CAB | ABC | ABC | Selection 2 |
**A**: As visualized in Figure 2, the proposed stochastic model with discrete latent variables discretizes the latent values into bits (zeros and ones) while training an auxiliary LSTM-based Hochreiter & Schmidhuber (1997) recurrent network to predict these bits autoregressively**B**: To make the predictive model more ... | CBA | BCA | ACB | CAB | Selection 3 |
**A**: Moreover, it’s important to consider that these terramechanics models, striving to predict robot-terrain interactions, often involve substantial computational costs due to their complexity [16]. Therefore, terramechanics methods are unsuitable for use in autonomous locomotion mode transition control directly, pa... | BAC | CAB | CAB | BCA | Selection 1 |
**A**: Our objective is to propose a model which allows the possibility of incorrect advice, with the objective of obtaining more realistic and robust online algorithms.**B**: Our motivation stems from observing that, unlike the real world, the advice under the known models is often closer to “fiat” than “recommendatio... | CBA | ABC | ABC | BAC | Selection 1 |
**A**: As said earlier, each chunk contained 10% of the subject’s writing history, a value that for some subjects could be just a single post while for others hundreds or even thousands of them**B**: Furthermore, the use of chunks assumes we know in advance all subject’s posts, which is not the case in real life scenar... | CBA | ACB | ABC | CAB | Selection 3 |
**A**: Since DEF-A enhances the generalization performance of DEF, we only consider DEF-A in this paper..
The momentum variant of DEF-A in (Xu and Huang, 2022) uses local momentum.**B**: Due to the larger compressed error introduced by RBGS compared with top-s𝑠sitalic_s when selecting the same number of components of ... | ACB | BCA | ABC | CAB | Selection 4 |
**A**: operation.**B**: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization**C**:
, where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks | CAB | BCA | CBA | ABC | Selection 3 |
**A**: All UAVs are altering strategies with a certain probability of ω𝜔\omegaitalic_ω, which is determined by τ𝜏\tauitalic_τ and m𝑚mitalic_m. τ𝜏\tauitalic_τ also presents the dynamic degree of scenarios. The chance of UAVs to make mistakes when altering strategies is determined by the dynamic degree as in PBLLA.
*... | CBA | ACB | BAC | CAB | Selection 4 |
**A**: , (a¯/b¯)i=ai/bisubscript¯𝑎¯𝑏𝑖subscript𝑎𝑖subscript𝑏𝑖(\overline{a}\,/\,\overline{b})_{i}=a_{i}/b_{i}( over¯ start_ARG italic_a end_ARG / over¯ start_ARG italic_b end_ARG ) start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT = italic_a start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT / italic_b start_POSTSUBSCRIPT... | CBA | CAB | BAC | ABC | Selection 1 |
**A**: Q-learning is among the most widely used reinforcement learning (RL) algorithms[4]**B**: 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]**C**: The Q-learning algorithm employs a table to estimate the... | ACB | BCA | ABC | CBA | Selection 3 |
**A**: Therefore, an interesting and potentially very useful research direction would be coming up with architectures and training methods that can incorporate spatial relationships from large medical images and volumes in the models.**B**: As such, they need to be processed either as patches or sub-volumes, making it ... | CBA | CAB | BAC | BCA | Selection 1 |
**A**:
Welbl (2014) and Biau et al. (2019) follow a similar strategy**B**: Independent training fits all networks one after the other and creates an ensemble of networks as a final classifier. Joint training concatenates all tree networks into one single network so that the output layer is connected to all leaf neuron... | BCA | CAB | ACB | ABC | Selection 3 |
**A**: Such a notion of robustness partially justifies the empirical advantages of KL-regularized policy optimization (Neu et al., 2017; Geist et al., 2019). To the best of our knowledge, OPPO is the first provably sample-efficient policy optimization algorithm that incorporates exploration.
**B**: In comparison, exist... | ACB | CBA | CAB | BAC | Selection 2 |
**A**: (2016) proposed deep compression, which extends the work Han et al**B**: (2015) by a parameter quantization and parameter sharing step, followed by Huffman coding to exploit the non-uniform weight distribution.
This approach yields a reduction in memory footprint by a factor of 35–49 and, consequently, a reducti... | ABC | CBA | BCA | ABC | Selection 3 |
**A**:
The notion of persistent homology arose from work by Frosini, Ferri, and Landi [40, 41], Robins [74], and Edelsbrunner [27, 37] and collaborators**B**: For example, Carlsson and de Silva [17] applied Vietoris-Rips persistent homology to topological estimation from point cloud data, and Ghrist and de Silva appli... | BCA | ABC | CAB | ACB | Selection 4 |
**A**: Essence is the capability to communicate concisely an overall essence of the data. Lastly, Time is the capability to reduce the necessary total time to respond to a large variety of queries about data. The operationalization of this conceptual approach led to the ICE-T evaluation form, where raters can give an a... | CBA | BAC | ACB | BCA | Selection 1 |
**A**: Therefore, improving their performance in a benchmark is not a reliable proof of performance competitiveness, but rather a convenient choice of comparison counterparts. Moreover, the experimental design is often not right: for example, the optima of the tested functions is often at the center of the domain searc... | CBA | BCA | BAC | BAC | Selection 2 |
**A**: Classical clustering models work poorly on large scale datasets. Instead, DEC and SpectralNet work better on the large scale datasets**B**: Although GAE-based models (GAE, MGAE, and GALA) achieve impressive results on graph type datasets, they fail on the general datasets, which is probably caused by the fact th... | CAB | CAB | ABC | BCA | Selection 3 |
**A**: This is not surprising, since the number of web servers is much larger than the others and it is recommended not to block ICMP to Web servers to allow for path MTU discovery**B**:
As can be seen in Table 3 the most applicable technique is PMTUD against Web servers, which applies to a bit more than 87% of the AS... | BAC | ABC | BCA | CAB | Selection 1 |
**A**: Several gas classifier models were placed in a setting with progressive sensor drift and were evaluated on samples from future contexts**B**:
The purpose of this study was to demonstrate that explicit representation of context can allow a classification system to adapt to sensor drift**C**: This task reflects t... | ABC | CAB | CBA | BAC | Selection 4 |
**A**: As an example, we prove in the following theorem that it is possible to adjoin a free generator to every self-similar semigroup without losing the self-similarity property and that the analogous statement for automaton semigroups holds as well**B**: The construction used to prove Theorem 6 can also be used to ob... | BAC | CAB | BCA | BCA | Selection 1 |
**A**: If the CPIG values are large, then it implies that large portion of correctly predicted samples were not properly grounded**B**:
In order to truly assess if existing methods are using relevant regions to produce correct answers, we use our proposed metric: Correctly Predicted but Improperly Grounded (CPIG)**C**... | ACB | BAC | CBA | ABC | Selection 2 |
**A**: We trained PrivBERT using dynamic masked language modelling (Liu et al., 2019) for 50k steps with a batch size of 512 using the gradient accumulation technique on two NVIDIA Titan RTX for 8 days with a peak learning rate of 8e-5. Other hyperparameters were set similar to RoBERTa.**B**:
We use the byte pair enco... | BAC | BCA | CAB | CBA | Selection 3 |
**A**: The same MDS projection is observable in varying stages with different legend ranges and diverse colors for each instance, depending on the selected performance metric.
The three steps in this figure demonstrate that we can reach both performant base models but also diverse algorithms by exploration of different... | ACB | CAB | CBA | CAB | Selection 1 |
**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... | BCA | BAC | CBA | BAC | Selection 1 |
**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... | ABC | CBA | BAC | CBA | Selection 1 |
**A**: This will be bootstrapped to the multi-color case in later sections**B**: We**C**: 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... | ACB | ABC | CBA | BCA | Selection 1 |
**A**: Moreover, in contrast to the NTK regime, the induced feature representation is able to deviate from the initial one and subsequently evolve into the globally optimal one, which corresponds to the global minimizer of the MSPBE. We further extend our analysis to soft Q-learning, which is connected to policy gradie... | ABC | CBA | BAC | CAB | Selection 4 |
**A**: In our work, we regard the outputs of stacked layers as a “vertical” sequence, and utilize the same gate mechanisms to selectively aggregate information from stacked Transformer layer outputs and to address the gradient vanishing issue of deep Transformers. LSTMs are able to capture long-distance relationships: ... | BCA | ACB | ACB | CAB | Selection 1 |
**A**: By**B**: Consider a monotone sentence
φ∈𝖥𝖮[σ]Struct(σ)𝜑𝖥𝖮subscriptdelimited-[]σStructσ\varphi\in\mathsf{FO}[\upsigma]_{\operatorname{Struct}(\upsigma)}italic_φ ∈ sansserif_FO [ roman_σ ] start_POSTSUBSCRIPT roman_Struct ( roman_σ ) end_POSTSUBSCRIPT**C**: Let (Ui)i∈Isubscriptsubscript𝑈𝑖𝑖𝐼(U_{i})_{i\in... | CAB | BCA | CBA | BCA | Selection 1 |
**A**: Evaluation Metrics: Crucially, evaluating the performance of different methods with reasonable metrics benefits experimental comparisons**B**: For the evaluation of the estimated distortion label, it is straightforward to employ the root mean square error (RMSE) between the estimated coefficients 𝒦^^𝒦\hat{\mat... | ACB | CBA | BCA | CAB | Selection 1 |
**A**: Figure 2 shows the learning curves of the five methods**B**: 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 ... | BAC | ABC | CAB | CAB | Selection 2 |
**A**: On a high level, the obstacle is that radius-minimization requires estimating the cost of each approximate solution; counter-intuitively, this may be harder than optimizing the cost (which is what is done in previous results)**B**:
Unfortunately, standard SAA approaches [26, 7] do not directly apply to radius m... | BCA | BCA | BCA | BAC | Selection 4 |
**A**: The random graph sequences in [12]-[15] are i.i.d. with connected and undirected mean graphs. In addition, additive communication noises are considered in [14]-[15].**B**: In [11], the local gradient noises are independent with bounded second-order moments and the graph sequence is i.i.d.
In [12]-[14], the (sub)... | BCA | CBA | BCA | BCA | Selection 2 |
**A**: Therefore, differing from Mondrian and Anatomy, increasing the level of protection of MuCo has little influence on the query results. In conclusion, MuCo can achieve the same level of protection as generalization does but with less information loss and more accurate query results. Note that, since we use the sum... | CAB | BCA | BAC | BCA | Selection 1 |
**A**: Table 2: PointRend’s step-by-step performance on our own validation set (splitted from the original training set)**B**: “MP Train” means more points training and “MP Test” means more points testing**C**: “P6 Feature” indicates adding P6 to default P2-P5 levels of FPN for both coarse prediction head and fine-grai... | ACB | BCA | BAC | ABC | Selection 4 |
**A**: This solves a question raised by Gady Kozma some time ago (see [K], comment from April 2, 2011)**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_POSTSUPERSCR... | BCA | BAC | ACB | BCA | Selection 2 |
**A**: For both cases, we present their respective regret bounds**B**: Detailed proofs are deferred to Appendix B. Note that our algorithms are all designed for inhomogeneous setting.**C**:
In this section, we describe our proposed algorithm LSVI-UCB-Restart, and discuss how to tune the hyper-parameters for cases when... | CAB | BCA | CAB | ABC | Selection 2 |
**A**: As a measure against fake news, the Protection from Online Falsehoods and Manipulation Act (POFMA) was passed on May 8, 2019, to empower the Singapore Government to more directly address falsehoods that hurt the public interest**B**: The rising attention of fake news in the local scene has motivated various rese... | BAC | BCA | BAC | CAB | Selection 2 |
**A**:
Our method represents a standard KG embedding approach capable of generating embeddings for various tasks**B**: While some methods attempt to address entity alignment by introducing a new relation, the results often demonstrate poor performance, as evidenced in [2, 27].**C**: This distinguishes it from most ind... | ACB | CAB | BCA | CAB | Selection 1 |
**A**: The goal of RL is to find a policy that maximizes the expected cumulative reward**B**: The gradient of vanilla policy gradient [16] is defined as
**C**: Policy gradient methods solve RL problem by iteratively following a parameterized policy, sampling data from the parameterized policy, and updating the paramete... | CAB | CBA | CAB | ACB | 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... | BAC | CBA | ACB | BCA | Selection 4 |
**A**: They aren’t really separating into nuisance and independent only.. they are also throwing away nuisance.**B**: Prior work in unsupervised DR learning suggests the objective of learning statistically independent factors of the latent space as means to obtain DR. The underlying assumption is that the latent variab... | ACB | BCA | ACB | CAB | Selection 4 |
**A**: Thus, signal cables require one transistor for switching action at the end**B**: When introducing the concept of an inverted signal pair of digital signals into a structural computer, the signals are paired, so a total of four wires are required to process the two auxiliary signals. This is defined as a double p... | BAC | BAC | BCA | CAB | Selection 3 |
**A**: In this paper, we propose a representation of such a group using the concept of linear representation defined through the Koopman operator.**B**: Given a finite subset of such permutations, we can compute a group generated by this set**C**:
A finite field, by definition, is a finite set, and the set of all perm... | CAB | BAC | CBA | ACB | Selection 3 |
**A**: It is also worth noting that the sets of views selected by stability selection in both gene expression data sets had low view selection stability. Ideally, selecting views based on their stability would lead to a set of selected views that is itself highly stable, but evidently this is not the case. It follows t... | ABC | ACB | CBA | ACB | Selection 3 |
**A**:
Concerning ROC AUC, the tree-based algorithms, CART and mCART, demonstrate superior performance compared to all the linear regression algorithms, as illustrated in Figure 4(a)**B**: The p𝑝pitalic_p-values in the figure reveal that each regression algorithm (excluding Lasso) exhibits significant improvement ove... | ACB | CBA | ABC | ACB | Selection 3 |
**A**: [2010], Li et al**B**: Next we show how using a global lower bound in form of κ𝜅\kappaitalic_κ (see Assumption 2) early in the analysis in the works Filippi et al**C**: [2017], Oh & Iyengar [2021] lead to loose prediction error upper bound. For this we first introduce a new notation:
| ABC | BAC | ACB | ABC | Selection 2 |
**A**: The temporal branch contains a Conv1d(3,1)Conv1d31\textrm{Conv1d}(3,1)Conv1d ( 3 , 1 )222For conciseness, we use Conv1d(m,n)Conv1d𝑚𝑛\textrm{Conv1d}(m,n)Conv1d ( italic_m , italic_n ) to represent 1-D convolutions with kernel size m𝑚mitalic_m and stride n𝑛nitalic_n. layer. In the graph branch, we build a gr... | ACB | ABC | CAB | ABC | Selection 3 |
**A**: Despite that, one possible improvement for VisEvol is to utilize parallel processing on powerful cloud servers. Moreover, we believe that the advancements in high-performance hardware and progressive VA and data science workflows [SPG14, TPB∗18] will be beneficial for VisEvol.
The users can also avoid extra comp... | ABC | CAB | CBA | ABC | Selection 3 |
**A**:
The current paper presents a consensus protocol specifically tailored to operate on a dynamic graph topology**B**: Consequently, our approach eliminates the reliance on conventional connectivity assumptions commonly found in the existing literature [27, 28, 29, 30, 31, 32].**C**: We establish that the protocol ... | ABC | CAB | ACB | CAB | Selection 3 |
**A**: In that case, in addition to linear point-to-point matching costs, quadratic costs for matching pairs of points on the first shape to pairs of points on the second shape are taken into account. Since pairs of points can be understood as edges in a graph, this corresponds to graph matching. Due to the NP-hardness... | BAC | CBA | CAB | ACB | Selection 1 |
**A**: We present the algorithm RecognizePG**B**: 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 path tree of G𝐺Gitalic_G by merging arbitrarily the clique path tree of every... | BAC | CAB | ABC | ACB | Selection 4 |
**A**: Before comparing these methods, we take some preprocessing to remove nodes that may have mixed memberships for community detection**B**: The smallest group with only 2 nodes in UKfaculty data is removed. Table 1 presents some basic information about the four datasets.
**C**: For the Polbooks data, nodes labeled ... | ABC | ACB | BCA | CBA | Selection 2 |
**A**: (2018); Boumal et al. (2018); Bécigneul and Ganea (2018); Zhang and Sra (2018); Sato et al. (2019); Zhou et al. (2019); Weber and Sra (2019) and the references therein.
Also see recent reviews (Ferreira et al., 2020; Hosseini and Sra, 2020)**B**: (2017); Agarwal et al. (2018); Zhang et al. (2018); Tripuraneni et... | CBA | BAC | BAC | CAB | Selection 1 |
**A**: However, as the joint action space grows exponentially with the number of agents, it is infeasible or costly in realistic deployment, and training emergent communication protocols also remains a challenging problem.
In addition, once the policy function is conditioned on not only the current agent’s observation ... | CAB | CBA | BCA | BAC | Selection 2 |
**A**: Specifically, in Section 6.1 we describe the benchmarks and the input generation model; in Section 6.2, we expand on the predictions and error measurement; and in Section 6.3, we present and discuss the main experimental results**B**:
In this section, we present an experimental evaluation of the performance of ... | BAC | ACB | ACB | BCA | Selection 1 |
**A**: We use standard measures for this task like Jensen-Shannon Divergence (JSD), coverage (COV), and minimum matching distance (MMD), where the last two measures are calculated for Chamfer (CD) and Earth-Mover (EMD) distances separately.
**B**: We examine the generative capabilities of the provided LoCondA model com... | CAB | ACB | BAC | ACB | Selection 1 |
**A**:
By using the standard restarts or regularization arguments, all the results of this paper have convex-concave or strongly convex-concave analogues**B**: Unfortunately, optimalilty w.r.t**C**: ε𝜀\varepsilonitalic_ε take places only for the convex-concave case not for the strongly convex-concave one.222The analy... | ACB | BCA | ABC | BCA | Selection 3 |
**A**: In [5] a unified perspective of the problem is presented. The authors show that the MCB problem is different in nature for each class. For example in [10] a remarkable reduction is constructed to prove that the MCB problem is NP-hard for the strictly fundamental class, while in [11] a polynomial time algorithm i... | CAB | CAB | BCA | CBA | Selection 4 |
**A**: This technique, which we briefly outline here, was specifically designed for complete intersection patterns**B**: A major part of this paper, all of Sections 3 and 4, is devoted to adapt it to handle the k𝑘kitalic_k-partite structure of colorful intersection patterns.**C**:
The proof of Theorem 2.1 is quite in... | BCA | CBA | ACB | CBA | Selection 1 |
**A**: To make our approach even more future-proof, we train this ML algorithm with the Bayesian Optimization package [73]. For this section, we validate our results with cross-validation using 8-folds, and we scan the hyperparameter space for 25 iterations, choosing the model with the best accuracy**B**: In this curre... | ABC | BCA | BAC | ABC | Selection 3 |
**A**: For the initialization phase needed to train the GPs in the Bayesian optimization, we select 20 samples over the whole range of MPC parameters, using Latin hypercube design of experiments**B**: After the initial learning phase the algorithm quickly finds the region where the simulation is feasible with respect t... | ABC | ACB | BCA | CBA | Selection 2 |
**A**: It is unclear how well methods would fare in presence of multiple types of bias, e.g., position or co-occurring objects/patterns, which are commonly present in real-world datasets. For some tasks it can be impossible to exhaustively enumerate all bias variables**B**: In addition, we posit that the commonly used ... | BCA | BAC | BCA | CAB | Selection 2 |
**A**: The second row in Tab. VII shows the result of PoG estimation methods**B**: \addedAFF-Net [57] and EFE [187] shows the best performance than other compared methods.
The third and fourth rows show the converted results**C**: Compared methods are designed for gaze direction estimation and we convert the result int... | BCA | BCA | BCA | ABC | Selection 4 |
**A**: The proposed method outperformed TL-based method using the same pre-trained models. This performance is explained by the fact that the fc layers of the pre-trained models are more dataset-specific features (generally pre-trained on ImageNet dataset) which is a very different dataset, thus, this strategy is not a... | ACB | CBA | ABC | BAC | Selection 2 |
**A**: In that case, sizes could be related to the grades of the adjoint modalities [Som21]. Furthermore, we are interested in generalizing to substructural, polymorphic, higher-kinded [DDMP21], and dependent types [CP96, KPB15].
**B**: Richer types: to mix linear [Pfe20], affine linear, non-linear, etc**C**: reference... | ABC | CAB | BCA | BAC | Selection 2 |
**A**: AFP mainly relies on cryptographic tools including public key cryptosystem and homomorphic encryption, in which the embedding operation is performed in the ciphertext domain so that only the user has assess to his/her own fingerprint**B**: A watermarking technique being able to safeguard the user’s rights while ... | CBA | BAC | ABC | BCA | Selection 2 |
**A**: We thus devise a novel technique to select the beneficial feature interactions, which is also to infer the graph structure. Then we adopt an attentional aggregation strategy to aggregate these selected beneficial interactions to update the feature representations.**B**: In summary, when dealing with feature inte... | CBA | CBA | CAB | ABC | Selection 3 |
**A**: [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 (B-FW) for simplicity.
**B**: [2022] is in essence the Frank-Wolfe algorithm with a modified version of the backtracking line sea... | BAC | CBA | BAC | ABC | Selection 2 |
**A**: As a consequence of such behavior of Algorithm 7, Backtrack-Stuck-Structures potentially reduces some active paths although those active paths can be extended, leading to some augmentations not be found. In this section we upper-bound the number of such “lost” augmentations.**B**: Algorithm 7 does not achieve th... | BAC | CBA | ABC | CAB | Selection 2 |
**A**: In this paper, we consider decentralized optimization over general directed networks and propose a novel Compressed Push-Pull method (CPP) that combines Push-Pull/𝒜ℬ𝒜ℬ\mathcal{A}\mathcal{B}caligraphic_A caligraphic_B with a general class of unbiased compression operators**B**: We show CPP achieves linear conv... | CBA | ABC | ACB | BCA | Selection 3 |
**A**: The following method (Algorithm 3) is also sharpened on the alternation of local iterations and communications, but it makes them more evenly**B**: Our first two methods make several iterations between communications when λ𝜆\lambdaitalic_λ is small (or vice versa, for big λ𝜆\lambdaitalic_λ make some communicat... | CAB | ACB | BCA | BAC | Selection 4 |
**A**:
The new solution concept MG(C)CE is rooted in the powerful principles of entropy and margin maximisation**B**: The MG(C)CE defines a family of unique solutions parameterized by ϵitalic-ϵ\epsilonitalic_ϵ, that can control for the properties of the distribution. We have compared it to other NE, CE, and α𝛼\alphai... | BAC | CBA | ABC | ACB | Selection 4 |
**A**: Bayes stability captures the concept that the results returned by a mechanism and the queries selected by the adaptive adversary are such that the queries behave similarly on the true data distribution and on the posterior distribution induced by those results**B**: In order to complete the triangle inequality, ... | ACB | BAC | BCA | CBA | Selection 2 |
**A**: Instead, fixed-parameter tractable (FPT) algorithms have a running time that scales polynomially with the input size, and which only depends exponentially on a problem parameter such as the solution size or treewidth. Hence an exponential speed-up of such algorithms cannot be explained by merely a decrease in in... | CBA | BCA | BCA | CAB | Selection 1 |
**A**: DESOBAv2 has 21,575 images with 28,573 valid object-shadow pairs.**B**:
To alleviate the burden of manually annotating masks and removing shadows, [96] design an automatic pipeline to construct shadow generation dataset and contributed a larger-scale dataset DESOBAv2**C**: Specifically, [96] employ the pretrain... | CAB | BAC | CBA | ACB | Selection 1 |
**A**: In Section III, we employ data mining tools to reveal and elucidate the correlations between contexts and service data. In Section IV, we conduct machine learning experiments, such as spatio-temporal predictions, transfer learning, and reinforcement learning on CityNet, to provide comprehensive benchmarking resu... | ACB | CAB | ACB | ACB | Selection 2 |
**A**: An ensemble average of 50 MC samples was used**B**: Mean-variance estimator: The Gaussian dropout-based mean-variance estimator from kendallgal was chosen because this model extends the dropout ensemble by explicitly incorporating uncertainty**C**: The dropout probability was optimized over the interval [0.05,0... | BAC | CBA | CBA | ACB | Selection 1 |
**A**: Then, a clustering algorithm is used to find a threshold for each piece adaptively**B**: Their CNN learns to predict the probability that each note belongs to the melody line**C**: Finally, the Bellman-Ford algorithm is adopted to pick a strictly monophonic melody line.
In contrast, we do not have postprocessing... | ACB | ABC | CAB | BAC | Selection 4 |
**A**: As it was stated in the proof of Lemma 2.2, while searching for a central vertex we always jump from a vertex to its neighbor in a way that decreases the largest remaining component by one**B**: Thus, if in the next iteration we start at exactly the neighbor of the previous central vertex, there can be only O(n... | ABC | BCA | CBA | ABC | Selection 2 |
**A**: In this article, we have investigated a DL-enabled semantic communication system for speech recognition, named DeepSC-SR, which aims to restore the text transcription by utilizing the text-related semantic features**B**: Particularly, we jointly design the semantic and channel coding to learn and extract the fea... | ACB | CBA | ABC | ACB | Selection 3 |
**A**: The existing 3D WSSS methods formulate the problem in different directions. [10] utilize dense 2D segmentation labels to supervise the training in 3D by projecting the 3D predictions onto the corresponding 2D labels. However, each 3D sample is projected to 2D in several views and each projected 2D image needs pi... | ACB | CBA | BCA | BAC | Selection 1 |
**A**: We use black box for ground-truth, red box for baseline results, and blue box for our results**B**: All the illustrated images are from the KITTI val set. Zoom in on the circles for more detailed comparison.
**C**: Qualitative results of our method for Bird’s-Eye-View | ABC | ACB | BCA | ABC | Selection 3 |
**A**: It contains 1,255 training images and 300 testing images.
CTW1500 [18] consists of curved and multi-oriented texts, all annotated by polygons, and tends to label long curved text**B**: It has 1,500 training and 1,000 testing images.**C**: Total-Text [19] consists of horizontal, multi-oriented, and curved text in... | BCA | CBA | CBA | ACB | Selection 1 |
**A**:
Assume that all IP addresses are logically divided into q𝑞qitalic_q subsets according to the value of the first part of an individual IP address**B**: Further assume are q𝑞qitalic_q computers for parallel computation, where the statistics collection task of each subset can be performed by an individual comput... | CAB | BAC | ABC | CBA | Selection 3 |
**A**: The work of G. Ju is supported in part by the National Key R & D Program of China (2017YFB1001604). The work of J**B**: Li is partially supported by the National Natural Science Foundation of China No. 11971221 and the Shenzhen Sci-Tech Fund No. RCJC20200714114556020, JCYJ20170818153840322 and JCYJ20190809150413... | BCA | ABC | CAB | BAC | Selection 1 |
**A**: The hubs orchestrate this information exchange every time they update their parameter blocks**B**: To execute the local iterations, clients need embeddings from the data from clients in other silos**C**: By waiting for several training iterations to exchange this information, the communication cost of the algori... | BAC | CBA | ACB | CBA | Selection 1 |
**A**: This development stemmed from their endeavor to extend the matrix singular value decomposition to the realm of tensors. For the purpose of clarification and distinction from other tensor product operations Golub2013matrix , this specific type of multiplication is referred to as tensor-tensor multiplication.**B**... | CBA | BAC | BCA | CAB | Selection 1 |
**A**: Furthermore, a Bi-directional Gated Feature Fusion module is introduced followed by a Contextual Feature Aggregation module to refine the results, with both semantically reasonable structures and detail-rich textures. Experiments show that this model is competent for this issue and outperforms the state-of-the-a... | CBA | CBA | CAB | CBA | Selection 3 |
**A**: Together with the binary symmetric channel (BSC), they are frequently used in coding theory and information theory because they are among the simplest channel models, and many problems in communication theory can be reduced to problems in a BEC. Here we consider more generally a q𝑞qitalic_q-ary erasure channel ... | ACB | BCA | ABC | CBA | Selection 4 |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 3