shuffled_text stringlengths 267 4.47k | A stringclasses 6
values | B stringclasses 6
values | C stringclasses 6
values | D stringclasses 6
values | label stringclasses 4
values |
|---|---|---|---|---|---|
**A**:
The key idea is to transform the diagonal matrix with the help of row and column operations into the identity matrix in a way similar to an algorithm to compute the elementary divisors of an integer matrix, as described for example in [23, Chapter 7, Section 3]**B**: Thus recording the row and and column operat... | ACB | ABC | CAB | BAC | Selection 1 |
**A**: When LOD or VMS methods are considered, high-contrast coefficients might slow down the exponential decay of the solutions, making the method not so practical**B**: Here in this paper, in the presence of rough coefficients, spectral techniques are employed to overcome such hurdle, and by solving local eigenvalue ... | ABC | CAB | BCA | BAC | Selection 3 |
**A**: Moreover, Alg-A is more stable than the alternatives.
During the iterations of Alg-CM, the coordinates of three corners and two midpoints of a P-stable triangle (see Figure 37) are maintained**B**: Alg-CM uses an involved subroutine (far more complicated than ours given in Algorithm 1) to update the coordinates ... | BCA | BCA | BCA | ACB | Selection 4 |
**A**: We call them “debunking words” e.g., hoax, rumor or not true. Our intuition is, that the attitude of doubting or denying events is in essence sufficient to distinguish rumors from news. What is more, this generalization augments the size of the crowd (covers more ’voting’ tweets), which is crucial, and thus cont... | ABC | ACB | CAB | ACB | Selection 3 |
**A**: and probit losses.
Assumption 1 implies**B**: 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**C**: Assumption 1 includes many comm... | CAB | CBA | CAB | ABC | Selection 2 |
**A**: But if we fit the models of the first few hours with limited data, the result of learning parameters is not so accurate**B**: As we can see except for the first one, the fitting results of other three are not good enough.
**C**: We show the performance of fitting these two model with only the first 10 hours twee... | CAB | ABC | BCA | ACB | Selection 4 |
**A**:
Results**B**: The accuracy for basic majority vote is high for imbalanced classes, yet it is lower at weighted F1. Our learned model achieves marginally better result at F1 metric.**C**: The baseline and the best results of our 1stsuperscript1𝑠𝑡1^{st}1 start_POSTSUPERSCRIPT italic_s italic_t end_POSTSUPERSCR... | ACB | BCA | BAC | CBA | Selection 1 |
**A**: The only difference happens to patient 10 and 12 whose intakes are earlier at day.
Further, patient 12 takse approx**B**: 3 times the average insulin dose of others in the morning.**C**: The insulin intakes tend to be more in the evening, when basal insulin is used by most of the patients | ACB | BAC | CBA | BCA | Selection 4 |
**A**: (2015). The prediction of fixation density maps does not require accurate class boundaries but still benefits from combined mid- to high-level feature responses Kümmerer et al**B**: For related visual tasks such as semantic segmentation, information distributed over convolutional layers at different levels of th... | ACB | CAB | BAC | BCA | Selection 3 |
**A**: Then, in Section 5, we show how Loc can be reduced to Pathwidth, which yields an approximation algorithm for computing the locality number; furthermore, we investigate the performance of direct greedy strategies for approximating the locality number. Finally, since we consider this of high importance independent... | CAB | BCA | ACB | ABC | Selection 1 |
**A**: To ensure exploration, SimPLe starts rollouts from randomly selected states taken from the real data buffer D. Figure 9 compares the baseline with an experiment without random starts and rollouts of length 1000100010001000 on Seaquest which shows much worse results without random starts.
**B**: Random starts**C*... | ABC | CAB | BAC | ACB | Selection 2 |
**A**: 9. The blue line illustrates the total energy consumed (in rolling locomotion mode), while the green line represents the ongoing cumulative energy consumption of the rear legs, indicating it did not exceed the threshold values set by the rear body climbing gait.**B**:
Figure 10: The Cricket robot tackles a step... | ABC | BAC | ACB | CAB | Selection 4 |
**A**: The algorithm classifies items according to their size. Tiny items have their size in the range (0,1/3]013(0,1/3]( 0 , 1 / 3 ], small items in (1/3,1/2]1312(1/3,1/2]( 1 / 3 , 1 / 2 ], critical items in (1/2,2/3]1223(1/2,2/3]( 1 / 2 , 2 / 3 ], and large items in (2/3,1]231(2/3,1]( 2 / 3 , 1 ]. In addition, the al... | ACB | BCA | CBA | BCA | Selection 1 |
**A**: As far as we know, the approach presented in [Dulac-Arnold et al., 2011] is the first to address a (sequential) text classification task as a Markov decision process (MDP) with virtually three possible actions: read (the next sentence), classify333In practice, this action is a collection of actions, one for each... | BCA | CAB | ACB | BCA | Selection 3 |
**A**: However, the theory about the convergence of DGC is still lacking**B**: Therefore, the momentum in DGC is a local momentum without global information.
**C**: Furthermore, although DGC combines momentum and error feedback, the momentum in DGC only accumulates stochastic gradients computed by each worker locally | ABC | BAC | BCA | ACB | Selection 4 |
**A**: operation.**B**:
, where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks**C**: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization | ACB | CAB | CBA | CBA | Selection 2 |
**A**:
In game theory, Nash Equilibrium (NE) is a special state that no UAV can gain more payoff by changing its strategy**B**: Thus, NE is an ideal solution for all UAVs in multi-UAV relay mission game [8]**C**: The potential game is usually used in analyzing the existence of NE. We first define the Pure Strategy Nas... | BCA | ABC | BAC | BAC | Selection 2 |
**A**: , for simulations with relatively high thermal diffusion**B**: italic_g **C**: of τLRsubscript𝜏𝐿𝑅\tau_{LR}italic_τ start_POSTSUBSCRIPT italic_L italic_R end_POSTSUBSCRIPT can be chosen as a simulation input when shorter-lived
CTs (e.g.,formulae-sequence𝑒𝑔e.g.,italic_e | ABC | CBA | CAB | ACB | Selection 2 |
**A**: The majority of analytical and empirical studies suggest that overestimation typically stems from the max operator used in the Q-learning value function**B**: This phenomenon introduces a positive bias that may lead to asymptotically sub-optimal policies, distorting the cumulative rewards**C**: Additionally, the... | BAC | CBA | ACB | ACB | Selection 1 |
**A**: To perform image segmentation in real-time and be able to process larger images or (sub) volumes in case of processing volumetric and high-resolution 2D images such as CT, MRI, and histopathology images, several methods have attempted to compress deep models. Weng et al**B**: (2019a) applied a neural architectur... | CAB | ABC | BCA | BAC | Selection 2 |
**A**: Finally, the output layer is connected to all leaf neurons and aggregates the results by implementing the leaf votes.
By using hyperbolic tangent and sigmoid functions, respectively, as activation functions between the layers, the generated network is differentiable and, thus, trainable with gradient-based optim... | CBA | CAB | BAC | BAC | Selection 1 |
**A**: In comparison, we focus on policy-based reinforcement learning, which is significantly less studied in theory. In particular, compared with the work of Yang and Wang (2019b, a); Jin et al. (2019); Ayoub et al. (2020); Zhou et al. (2020), which focuses on value-based reinforcement learning, OPPO attains the same ... | CAB | CBA | ABC | BCA | Selection 1 |
**A**: (2018b) introduced a ResNet-inspired architecture called ShuffleNet which employs 1×1111\times 11 × 1 grouped convolutions since 1×1111\times 11 × 1 convolutions have been identified as computational bottlenecks in previous works, e.g., see Howard et al. (2017a).**B**: Although this reduces the expressiveness of... | CBA | CAB | ABC | BCA | Selection 2 |
**A**: Virk provided a proof of the Corollary below which takes place at the simplicial level**B**: The proof we give below exploits the hyperconvexity properties of L∞(X)superscript𝐿𝑋L^{\infty}(X)italic_L start_POSTSUPERSCRIPT ∞ end_POSTSUPERSCRIPT ( italic_X ) and also our isomophism theorem, Theorem 1. Given our ... | BAC | CBA | BCA | ABC | Selection 3 |
**A**: In summary, although there is a superficial similarity between the two techniques regarding how the user interacts with the scatterplot, their goals and their inner workings are quite different. Since t-viSNE adopts an approach of combining many different coordinated views, it is important for the Dimension Corr... | BAC | CBA | ACB | ACB | Selection 2 |
**A**: In [20], authors present an analysis of the Cuckoo Search, one of the most well-known algorithms in the literature**B**: The Cuckoo Search is just an evolutionary strategy with some parts of DE, algorithms from the last century.
**C**: Their review of this algorithm based on its usefulness, novelty and sound mot... | BAC | ABC | BCA | ACB | Selection 4 |
**A**: Three deep clustering methods for general data, DEC [8] DFKM [9], and SpectralNet [7], also serve as an important baseline**B**: All codes are downloaded from the homepages of authors.
**C**: Besides, four GAE-based methods are used, including GAE [20], MGAE [21], GALA [32], and SDCN [31] | BAC | BAC | ACB | BAC | Selection 3 |
**A**: We next explain each measurement technique. In our measurements in Section 4 we compare the success and applicability of each technique.
**B**: The results from the tests are stored in the backend database**C**: The GUI displays the results of the measurements at https://smap.cad.sit.fraunhofer.de | CAB | ABC | ABC | BCA | Selection 1 |
**A**: Experiments in this paper used the gas sensor drift array dataset [7]. The data consists of 10 sequential collection periods, called batches**B**: Every batch contains between 161161161161 to 3,60036003{,}6003 , 600 samples, and each sample is represented by a 128-dimensional feature vector; 8 features each from... | CBA | CAB | ACB | ABC | Selection 4 |
**A**: The construction used to prove Theorem 6 can also be used to obtain results which are not immediate corollaries of the theorem (or its corollary for automaton semigroups in 8)**B**: As an example, we prove in the following theorem that it is possible to adjoin a free generator to every self-similar semigroup wit... | BCA | CAB | BAC | ABC | Selection 4 |
**A**: It is also interesting to note that the drop in training accuracy is lower with this regularization scheme as compared to the state-of-the-art methods**B**: We do not observe such behavior in any of the methods, indicating that they are not producing right answers for the right reasons.
**C**: Of course, if any ... | ACB | BAC | BCA | CBA | Selection 1 |
**A**: Since Roberta accepts a maximum of 512 tokens as input, only the first 512 tokens of text from the documents were used for training while the rest was discarded. As shown in the analysis section, the average length of a privacy policy in terms of the number of words is 1,871. Thus 512 tokens would take into acco... | CAB | BAC | CBA | ACB | Selection 1 |
**A**: 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 focuses on construction of better-performing ensembles by combining multiple algorithms and using appropriate performance metrics.**B**: Spec... | ABC | CBA | ACB | BCA | Selection 2 |
**A**: Task similarity. In Persona and Weibo, each task is a set of dialogues for one user, so tasks are different from each other. We shuffle the samples and randomly divide tasks to construct the setting that tasks are similar to each other**B**: For a fair comparison, each task on this setting also has 120 and 1200 ... | CAB | ABC | CAB | BCA | Selection 2 |
**A**: The newly proposed CA codebook can fully exploit the potentials of the DRE-covered CCA to offer full spatial coverage. Moreover, the corresponding codeword selection scheme is also carefully designed to facilitate fast multi-UAV beam tracking/communication in the considered CA-enabled UAV mmWave network.**B**:
... | ABC | ABC | CAB | BCA | 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... | BAC | CBA | ABC | BAC | Selection 2 |
**A**: See Geist and Pietquin (2013); Bertsekas (2019) for a detailed survey. When the value function approximator is an overparameterized multi-layer neural network, Cai et al. (2019) prove that TD converges to the globally optimal solution in the NTK regime. See also the independent work of Brandfonbrener and
Bruna (... | CAB | BCA | ACB | BCA | Selection 1 |
**A**: Shen et al. (2018) propose a densely connected NMT architecture to create new features with dense connections. Wang et al**B**: (2018) propose a multi-layer representation fusion approach to learning a better representation from the layer stack. Dou et al. (2018) simultaneously expose all layer representations w... | CAB | CAB | BCA | BAC | Selection 3 |
**A**: pre-spectral space**B**: Recall that ⟨Y,τY,𝒦∘(Y)⟩𝑌subscriptτ𝑌superscript𝒦𝑌\langle Y,\uptau_{Y},\mathcal{K}^{\circ}\!\left(Y\right)\rangle⟨ italic_Y , roman_τ start_POSTSUBSCRIPT italic_Y end_POSTSUBSCRIPT , caligraphic_K start_POSTSUPERSCRIPT ∘ end_POSTSUPERSCRIPT ( italic_Y ) ⟩ is a lpps**C**: We are goin... | BCA | ABC | BAC | CBA | Selection 2 |
**A**: 7, the ordinal distortion estimation achieves the fastest convergence and best performance on the validation dataset**B**: (3) From the loss curves in Fig**C**: It is also worth to note that the ordinal distortion estimation already performs well on the validation at the first twenty epochs, which verifies that ... | BAC | CBA | ACB | ACB | Selection 1 |
**A**: Table 3 shows the training time per epoch of SNGM with different batch sizes**B**: Hence, compared to other results, SNGM requires more training time for the batch size of 128. Furthermore, we can observe that the training time decreases with the increasing batch size.
**C**: When B=128𝐵128B=128italic_B = 128, ... | BAC | CBA | ACB | CAB | Selection 3 |
**A**: The black-box model is motivated by data-driven applications where specific knowledge of the distribution is unknown but we have the ability to sample or simulate from the distribution. To our knowledge, radius minimization has not been previously considered in the two-stage stochastic paradigm**B**: Most prior ... | CBA | CAB | BCA | ABC | Selection 3 |
**A**: graph sequences as in [12]-[15],
and additive and multiplicative communication noises may co-exist in communication links ([21]).**B**: Both the weights of different edges in the network graphs at the same time instant and the network graphs at different time instants may be mutually dependent.) rather than i.i.... | ACB | CBA | ABC | ABC | 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**: However, generalization hardly preserves the distributions of original QI values that always causes a huge cost of pro... | BAC | ABC | CBA | ACB | Selection 4 |
**A**: (2020) on COCO. In SOLOv2, the unified mask feature branch is dynamically convoluted by learned kernels, and the adaptively generated mask for each location benefits from the whole image view instead of cropped region proposals like HTC. Using ResNeXt101-64x4d plugined with DCN and GC block, SOLOv2 achieves 75.2... | CBA | BAC | ACB | ABC | Selection 1 |
**A**: More specifically, we proved**B**: This solves a question raised by Gady Kozma some time ago (see [K], comment from April 2, 2011)**C**:
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\... | BAC | CAB | BCA | CBA | Selection 4 |
**A**: Section 6 shows our experiment results. Section 7 concludes the paper and discusses some future directions. All detailed proofs can be found in Appendices.**B**: Section 3 establishes the minimax regret lower bound for nonstationary linear MDPs. Section 4 and Section 5 present our algorithms LSVI-UCB-Restart, Ad... | BCA | ABC | CBA | ABC | Selection 3 |
**A**:
In general, respondents possess a competent level of digital literacy skills with a majority exercising good news sharing practices**B**: That respondents show strong trust and reliance on government communication platforms, such as official websites and hotlines, signifies the relatively strong faith that Sing... | BCA | BCA | ACB | CBA | Selection 3 |
**A**: However, this input embedding can still accumulate knowledge by participating in the aggregations of its neighbors. The acquired information may not necessarily reside in the same dimension for a pair of aligned entities at this layer, which accounts for the comparatively lower performance of this layer**B**:
T... | ABC | BAC | ACB | ACB | Selection 2 |
**A**: The Level 1111 of the game has different scenarios of day and night. We train all methods from scratch in the Level 1111. We refer to Fig. 8(b) for the evaluation curves of extrinsic rewards. The proposed VDM shows similar performance to RFM, while VDM’s learning curve is smoother and more stable.**B**: We take ... | CBA | ACB | BAC | BAC | Selection 1 |
**A**: The observations made in 2D remain valid**B**: The polynomial convergence rates of Floater-Hormann and all**C**: 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 co... | CAB | CAB | CBA | ACB | 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... | BAC | CBA | CBA | ABC | Selection 1 |
**A**:
DFS (Depth First Search) verifies that the output is possible for the actual Pin connection state**B**: As described above, the output is determined by the 3-pin input, so we will enter 1 with the A2 and A1 connections, the B2 and B1 connections (the reverse is treated as 0), and the corresponding output will b... | BCA | ACB | ABC | BCA | Selection 3 |
**A**: Given a finite subset of such permutations, we can compute a group generated by this set**B**:
A finite field, by definition, is a finite set, and the set of all permutation polynomials over the finite field forms a group under composition**C**: In this paper, we propose a representation of such a group using t... | BCA | BCA | BAC | CAB | Selection 3 |
**A**: In the other 5 views, we randomly determine 50% of the features to have a relationship with the outcome. The relationship between features and response is determined by a logistic regression model, where each feature related to the outcome is given a regression weight. In the setting with 30 views, we use the sa... | CAB | ACB | BAC | BCA | Selection 4 |
**A**: Independent variables refer to the variables that are not linked to any other variables in a Bayesian network**B**: IndepVar: the percentage of independent variables**C**: Therefore, IndepVar characteristic is calculated as the percentage of variables with no PC variables.
| CBA | BCA | BCA | BAC | Selection 4 |
**A**: [2020] Faury et al**B**: [2020] use a bonus term for optimization in each round, and their algorithm performs non-trivial projections on the admissible log-odds. While we do reuse the Bernstein-style concentration inequality as proposed by them, their results do not seem to extend directly to the MNL setting wit... | CBA | CAB | ABC | BCA | Selection 4 |
**A**: 3) VSGN shows obvious improvement on short actions over other concurrent methods, and also achieves new state-of-the-art overall performance**B**: On ActivityNet-v1.3, VSGN reaches an average mAP of 35.07%, compared to the previous best score 34.26% under the same features.
**C**: On THUMOS-14, VSGN reaches 52.4... | ABC | CAB | ABC | ACB | Selection 4 |
**A**: The second expert (E2) is a senior researcher in software engineering and applied ML working in a government research institute and as an adjunct professor. He has worked with ML for the past 7.5 years, and 2.5 years with ensemble learning**B**: The third expert (E3) is an ML engineer and manager in a large mult... | BCA | BAC | BAC | CBA | Selection 1 |
**A**: A useful extension of this research may involve imposing safety constraints on the density distribution of the swarm, such as density upper bounds or density rate bounds**B**: For the probabilistic swarm guidance application, removing the assumption that agents have access to density values of their own and neig... | CBA | BAC | ACB | BCA | Selection 2 |
**A**:
Shape matching can be formulated as bringing points defined on one shape into correspondence with points on another shape**B**: A simple mathematical formulation for doing so is the linear assignment problem (LAP) [49], where a linear cost function is optimised over the set of permutation matrices**C**: The obj... | BCA | ACB | ABC | ACB | Selection 3 |
**A**:
The recognition algorithm RecognizePG for path graph is mainly built on path graphs’ characterization in [1]. This characterization decomposes the input graph G𝐺Gitalic_G by clique separators as in [18], then at the recursive step one has to find a proper vertex coloring of an antipodality graph satisfying som... | BCA | ABC | BCA | BCA | Selection 2 |
**A**: Subfigure 1(b) suggests that Mixed-SLIM significantly outperforms Mixed-SCORE, OCCAM, and GeoNMF under the DCMM setting. It is interesting to find that only Mixed-SLIM enjoys better performances as the fraction of pure nodes increases under the DCMM setting.**B**: From the results in subfigure 1(a), it can be fo... | ABC | BCA | CAB | CBA | Selection 4 |
**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... | ABC | ACB | CBA | BCA | Selection 3 |
**A**: 2
illustrates a standard 4-phase setting: "north-south-straight", "north-south-left", "east-west-straight" and "east-west-left", "north-south-straight" means that the signal on the corresponding lanes are green. Note that the signal on the right-turn lanes is always green for consistency with real world.**B**: P... | ACB | BAC | CAB | BAC | Selection 3 |
**A**: Namely, the input sequence is the concatenation of n/50000𝑛50000n/50000italic_n / 50000 subsequences**B**: For Weibull benchmarks, each subsequence is a Weibull distribution, whose shape parameter is chosen uniformly at random from [1.0,4.0]1.04.0[1.0,4.0][ 1.0 , 4.0 ]. For BPPLIB benchmarks, each subsequence i... | BCA | ABC | ABC | ABC | Selection 1 |
**A**:
Using this formulation, charts are trained to approximate the target surface as closely as possible**B**: If one of them fails to cover the neighborhood of p𝑝pitalic_p properly, then no other patch will fix that part.**C**: However, it does not consider the stitching process itself - no information is shared b... | ABC | BCA | CAB | ACB | Selection 4 |
**A**: Unfortunately, optimalilty w.r.t**B**:
By using the standard restarts or regularization arguments, all the results of this paper have convex-concave or strongly convex-concave analogues**C**: ε𝜀\varepsilonitalic_ε take places only for the convex-concave case not for the strongly convex-concave one.222The analy... | BAC | ABC | ABC | ABC | Selection 1 |
**A**: We proceed by trying to find a counterexample based on our previous observations**B**: In this section we present some experimental results to reinforce
Conjecture 14**C**: In the first part, we focus on the complete analysis of small graphs, that is: graphs of at most 9 nodes. In the second part, we analyze lar... | CAB | BAC | CBA | CAB | Selection 2 |
**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... | CBA | ABC | BCA | ABC | Selection 3 |
**A**: Practitioners often spend a substantial amount of time experimenting with custom combinations of features or utilizing algorithmic feature generation [11, 2]**B**: In several application domains, algorithms have been useful for creating high-level features from data elements related to speech [13], real-time sen... | CAB | CAB | CAB | BCA | Selection 4 |
**A**: We compare three schemes: manual tuning of the MPCC parameters for fixed low level controller gains, Tuning of MPCC parameters through Bayesian optimization, and joint tuning of the MPCC- and the low-level cascade controller parameters using Bayesian optimization.**B**:
We use two geometries to evaluate the per... | ABC | CAB | ACB | ABC | Selection 2 |
**A**:
Recently, many methods have been proposed to make neural networks bias resistant**B**: These methods can be grouped into two types: 1) those that assume the bias variables e.g., the gender label in CelebA, are explicitly annotated and can be accessed during training [55, 55, 69, 37] and, 2) those that do not r... | ACB | CBA | ABC | CAB | Selection 3 |
**A**: Unlike the conventional gaze estimation methods that requires dedicated devices, the deep learning-based approaches regress the gaze from the eye appearance captured by web cameras**B**: In this survey, we present a comprehensive overview of deep learning-based gaze estimation methods**C**: This makes it easy to... | CBA | ACB | BAC | CBA | Selection 3 |
**A**:
We have tested the face recognizer presented in luttrell2018deep that achieved a good recognition accuracy on two subsets of the FERET database phillips1998feret **B**: This technique is based on transfer learning (TL) which employs pre-trained models and fine-tuning them to recognize masked faces from RMFRD a... | ABC | ACB | ACB | CBA | Selection 1 |
**A**: As we mentioned in the introduction, we use unbounded quantification [Vez15] in lieu of transfinite sizes to represent (co)data of arbitrary height and depth. However, the state of the art [Abe12, AP16, CLB23] supports polymorphic, higher-kinded, and dependent types, which we aim to incorporate in future work.
*... | BAC | CBA | ABC | ABC | Selection 2 |
**A**: The owner-side efficiency and scalability performance of FairCMS-II are directly inherited from FairCMS-I, and the achievement of the three security goals of FairCMS-II is also shown in Section VI**B**: Comparing to FairCMS-I, it is easy to see that in FairCMS-II the cloud’s overhead is increased considerably du... | BCA | ACB | CAB | ABC | Selection 4 |
**A**: As a consequence, we can model only these beneficial interactions with the next interaction aggregation component**B**: To check the necessity of this component, we remove this components, so that all pair of feature interactions are modeled as a fully-connected graph.**C**:
GraphFM(-S): interaction selection i... | CBA | ACB | BCA | CAB | Selection 3 |
**A**: We can make use of the proof of convergence in primal gap to prove linear convergence in Frank-Wolfe gap**B**: [2019] but already implicitly used earlier in Lacoste-Julien & Jaggi [2015] as:
**C**: In order to do so, we recall a quantity formally defined in Kerdreux et al | CAB | BAC | ACB | CAB | Selection 3 |
**A**: We call an algorithm an α𝛼\alphaitalic_α-approximation if the matching has a size at least 1/α1𝛼1/\alpha1 / italic_α times the optimum matching.**B**:
It is known that finding an exact matching requires linear space in the size of the graph and hence it is not possible to find an exact maximum matching in the... | ABC | CAB | ACB | BCA | Selection 2 |
**A**: To reduce the error from compression, some works [48, 49, 50] increase compression accuracy as the iteration grows to guarantee the convergence**B**: Techniques to remedy this increased communication costs include gradient difference compression [34, 51, 52] and error compensation [37, 53, 54], which enjoy bette... | CBA | BAC | ACB | ABC | Selection 3 |
**A**: While the centralized architecture consists of master-server that connected with all devices which communicate to the central server.
But in theory, the centralized case is similar to decentralized with a complete computational graph. If we set W𝑊Witalic_W to the Laplacian of a complete graph, it is easy to ver... | BCA | BCA | CAB | CBA | Selection 3 |
**A**: It gives similar solutions to NE in the two-player, constant-sum setting, however it is not directly related to NE or (C)CE**B**: An important area of related work is α𝛼\alphaitalic_α-Rank (Omidshafiei et al., 2019) which also aims to provide a tractable alternative solution in normal form games**C**: α𝛼\alpha... | BCA | BAC | CAB | BCA | Selection 2 |
**A**: In Section 3, we provide a tight measure of the level of overfitting of some query with respect to previous responses**B**:
The contribution of this paper is two-fold**C**: In Sections 4 and 5, we demonstrate a toolkit to utilize this measure, and use it to prove new generalization properties of fundamental noi... | ABC | CAB | BCA | BAC | Selection 4 |
**A**: A substantial theoretical framework has been built around the definition of kernelization [17, 22, 27, 29, 31]**B**: It includes deep techniques for obtaining kernelization algorithms [10, 28, 39, 43], as well as tools for ruling out the existence of small kernelizations [11, 19, 23, 30, 32] under complexity-the... | ABC | ACB | BAC | BAC | Selection 1 |
**A**: In the right subfigure, we provide the illustration of three ways to construct image harmonization dataset.**B**:
Figure 10: In the left subfigure, we summarize three ways to construct image harmonization dataset and list the corresponding datasets: RealHM [60], iHarmony4 [9] (HCOCO, HFlickr, HAdobe5k, Hday2nig... | ACB | ABC | CAB | CBA | Selection 3 |
**A**: To address this challenge, we leverage the data available in CityNet and present benchmarks for the taxi dispatching task**B**: In this task, operators are responsible for dispatching available taxis to waiting passengers in real-time with the objective of maximizing the long-term total revenue of the taxi syste... | CAB | ACB | BCA | ACB | Selection 3 |
**A**: the interval width one would obtain when ignoring the underlying features in the data set. If this quantity is smaller than one, the model is able to improve on a simple featureless estimator. From this point of view, it is clear that the models do not perform as well as could be thought on first sight for the f... | CBA | ABC | ABC | CAB | Selection 4 |
**A**: 3 presents the normalised confusion tables for three-class melody classification, illustrating distinct performance characteristics among the models**B**: Fig**C**: We note that the baseline exhibits a tendency to conflate vocal melody (M1) and instrumental melody (M2), whereas our model outperforms the RNN-base... | ACB | ACB | BAC | ABC | Selection 3 |
**A**: Thus, if in the next iteration we start at exactly the neighbor of the previous central vertex, there can be only O(n)𝑂𝑛O(n)italic_O ( italic_n ) such jumps in total.
**B**: As it was stated in the proof of Lemma 2.2, while searching for a central vertex we always jump from a vertex to its neighbor in a way t... | BAC | CBA | ACB | ABC | Selection 2 |
**A**:
The second benchmark is a traditional communication system to transmit text signals, named text transceiver**B**: For the system, the Huffman coding[24] is employed for text source coding, the settings of channel coding and modulation are same as that in benchmark 1. In addition, Deep Speech 2 model is utilized... | ACB | CBA | ABC | BCA | Selection 1 |
**A**: This specific problem is a well-documented challenge, and its presence is notable even in methods that employ full supervision. The inherent limitations of our method, stemming from its lack of comprehensive supervision, further exacerbate its ability to effectively navigate and resolve this particular challenge... | CAB | ABC | CBA | CBA | Selection 1 |
**A**:
Figure 5: Qualitative results of our method for multi-class 3D object detection**B**: All illustrated images are from the KITTI test set. Zoom in the image for more details.**C**: We use orange box for cars, purple box for pedestrians, and green box for cyclists | BAC | ACB | BCA | CBA | Selection 2 |
**A**: ICDAR2015 [44] includes multi-orientated and small-scale text instances. Its ground truth is annotated with word-level quadrangles**B**: It contains 300 training images and 200 testing images with word-level annotation. Here, we follow the previous methods [35, 8] and add 400 training images from TR400 [46] to e... | CAB | ABC | BCA | ACB | Selection 4 |
**A**: The first three parts of the IP address can be mapped into the corresponding position of the element in a particular memory block of the first layer according to the individual values of the three parts. We allocate a memory block in the other layer for the IP address when its first three parts are initially giv... | CBA | BCA | ABC | BAC | Selection 2 |
**A**: The authors would like to thank Mingjian Ding, and Baoxuan Zhu for providing an alternative proof of the Hurwitz stability of polynomials (25). They also thank Jarle Sogn for communicating on Schur complement based preconditioners.
The work of M. Cai is partially supported by the NIH-RCMI grant through 347 U54MD... | CBA | BCA | ACB | BAC | Selection 3 |
**A**: For CIFAR-10 we search for a learning rate in the range [0.0001, 0.00001], for MIMIC-III we search in the range [0.1, 0.001], and for ModelNet40 we search in the range [0.001, 0.00005]**B**:
In each experiment, for each value of Q𝑄Qitalic_Q, we choose the learning rate using a grid search**C**: For each Q𝑄Qit... | CBA | ACB | BCA | BAC | Selection 4 |
**A**: We investigate various aspects of pseudospectra theory, exploring different definitions of tensor ε𝜀\varepsilonitalic_ε-pseudospectra and discussing their properties**B**: Additionally, we complement our analysis by presenting visualizations that depict the ε𝜀\varepsilonitalic_ε-pseudospectra via some numerica... | CAB | CBA | BAC | BCA | Selection 4 |
**A**: At the encoding stage, the corrupted image and its corresponding edge map are individually projected into the latent space, where the left branch focuses on texture features and the right branch targets structure features**B**: At the decoding stage, the texture decoder synthesizes structure-constrained textures... | BAC | ABC | CAB | BCA | Selection 4 |
**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... | ACB | CBA | ABC | BCA | Selection 1 |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 3