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**A**: In an implementation the code for the Algorithms 4–7 would be inserted into Algorithm 3 in the lines where they are called**B**: 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 th... | CAB | BCA | CAB | CAB | Selection 2 |
**A**: Here in this paper, in the presence of rough coefficients, spectral techniques are employed to overcome such hurdle, and by solving local eigenvalue problems we define a space where the exponential decay of solutions is insensitive to high-contrast coefficients. Additionally, the spectral techniques remove
macro... | BCA | BCA | BCA | CAB | Selection 4 |
**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**: These coordinates are computed somehow and their true values can differ from their values stored in the computer**C... | CAB | BAC | CAB | ABC | Selection 4 |
**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 | BCA | ABC | CAB | CBA | Selection 3 |
**A**: and probit losses.
Assumption 1 implies**B**: Assumption 1 includes many common loss functions, including the logistic, exp-loss222The exp-loss does not have a global β𝛽\betaitalic_β smoothness parameter**C**: However, if we initialize with η<1/ℒ(𝐰(0))𝜂1ℒ𝐰0\eta<1/\mathcal{L}(\mathbf{w}(0))italic_η < 1 / ca... | BAC | ABC | ACB | CAB | Selection 4 |
**A**:
In this work, we present a deep analysis on the feature variants over 48 hours for the rumor detection task**B**: The results show that the low-level hidden representation of tweets feature is at least the second best features over time**C**: We also derive explanations on the low performance of supposed-to-be-... | ABC | ACB | BCA | ACB | Selection 1 |
**A**: Multi-Criteria Learning. Our task is to minimize the global relevance loss function, which evaluates the overall training error, instead of assuming the independent loss function, that does not consider the correlation and overlap between models**B**: We modified the objective function of RankSVM following our g... | CAB | CBA | ABC | ACB | Selection 4 |
**A**: 3 times the average insulin dose of others in the morning.**B**: The only difference happens to patient 10 and 12 whose intakes are earlier at day.
Further, patient 12 takse approx**C**: The insulin intakes tend to be more in the evening, when basal insulin is used by most of the patients | ACB | CBA | ACB | ACB | Selection 2 |
**A**: (2018). Defining the problem of saliency prediction in a probabilistic framework also enables fair model ranking on public benchmarks for the MIT1003, CAT2000, and SALICON datasets Kümmerer et al**B**: (2018). As a consequence, we evaluated our estimated gaze distributions without applying any metric-specific po... | BAC | BCA | CAB | CAB | Selection 2 |
**A**:
In this section, we introduce polynomial-time reductions from the problem of computing the locality number of a word to the problem of computing the cutwidth of a graph, and vice versa**B**: We also discuss the approximation-preserving properties of our reductions, which shall be important later on.**C**: This ... | ABC | CBA | ACB | CAB | Selection 3 |
**A**: The results are on a par with SimPLe – both of the model-free methods are better in 13 games, while SimPLe is better in the other 13 out of the total 26 games tested (note that in Section 4.2 van Hasselt et al. (2019) compares with the results of our first preprint, later improved).**B**: In our empirical evalua... | BCA | BAC | CAB | BAC | Selection 3 |
**A**:
In the literature review, Gorilla [2] is able to switch between bipedal and quadrupedal walking locomotion modes autonomously using criteria developed based on motion efficiency and stability margin. WorkPartner [8] demonstrated its capability to seamlessly transition between two locomotion modes: rolling and r... | ABC | BAC | BAC | BCA | Selection 1 |
**A**: More specifically, given an advice string of size k𝑘kitalic_k, let η𝜂\etaitalic_η denote the number of erroneous bits (which may be not known to the algorithm)**B**:
In future work, we would like to expand the model so as to incorporate, into the analysis, the concept of advice error**C**: In this setting, th... | BAC | CAB | ABC | ACB | Selection 1 |
**A**: Therefore, in this new (more realistic) scenario, subjects were processed one writing (post) at the time (in a stream-like way) and not using chunks.
**B**: 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 hund... | CAB | CBA | ACB | CBA | Selection 1 |
**A**: To the best of our knowledge, this is the first work that introduces global momentum for sparse communication in DMSGD**B**:
In this paper, we propose a novel method, called global momentum compression (GMC), for sparse communication in distributed learning**C**: Furthermore, to enhance the convergence performa... | BAC | ACB | BCA | CAB | Selection 1 |
**A**:
, where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks**B**: operation.**C**: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization | BAC | CAB | BCA | ACB | Selection 4 |
**A**:
In this part, we investigate the influence of environment dynamic on the network states. With different scenarios’ dynamic degree τ∈(0,∞)𝜏0\tau\in(0,\infty)italic_τ ∈ ( 0 , ∞ ), PBLLA and SPBLLA will converge to the maximizer of goal function with different altering strategy probability**B**: Fig. 6 presents t... | CAB | CAB | ABC | BAC | Selection 3 |
**A**: italic_g **B**: The symbol ∗*∗ between two matrices (e.g.,formulae-sequence𝑒𝑔e.g.,italic_e **C**: , C¯¯=A¯¯∗B¯¯¯¯𝐶¯¯𝐴¯¯𝐵\overline{\overline{C}}=\overline{\overline{A}}\,*\,\overline{\overline{B}}over¯ start_ARG over¯ start_ARG italic_C end_ARG end_ARG = over¯ start_ARG over¯ start_ARG italic_A end_ARG end_A... | ACB | CBA | ABC | BAC | Selection 4 |
**A**: However, our experiments were limited to simple problems and environments, utilizing small network architectures and only two Dropout methods.
**B**: In this study, we proposed and experimentally analyzed the benefits of incorporating the Dropout technique into the DQN algorithm to stabilize training, enhance pe... | BAC | BCA | CAB | CBA | Selection 3 |
**A**: Currently, deep models receive matrices of intensity values, and usually, they are not forced to learn prior information. Without explicit reinforcement, the models might still learn object relations to some extent. However, it is difficult to interpret a learned strategy.**B**: Encoding prior knowledge in medic... | ABC | ACB | CBA | ABC | Selection 3 |
**A**: Here, we additionally include decision trees, support vector machines, random forests, and neural networks in the comparison**B**: The overall performance of each method is summarized in the last column.
For neural random forest imitation, a network architecture with 128128128128 neurons in both hidden layers is... | ACB | BCA | CBA | CAB | Selection 1 |
**A**: Such an exploration-exploitation tradeoff is better captured by the aforementioned statistical question regarding the regret or sample complexity, which remains even more challenging to answer than the computational question**B**: In a more practical setting, the agent sequentially explores the state space, and ... | BCA | BAC | ABC | CBA | Selection 2 |
**A**: They are not suited to execute generic compressed models and are therefore not included in the following experiments.
**B**: While domain-specific accelerators, such as Google’s TPU, excel in their specific performance, they are usually limited to a set of specific operations and are neither flexible in terms of... | CAB | CBA | BCA | ABC | Selection 1 |
**A**: Observe that “strictly negative” sectional curvature is a necessary condition (for example, consider the Euclidean plane ℝ2superscriptℝ2\mathbb{R}^{2}blackboard_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT).
**B**: All compact Riemannian manifolds are trivially hyperbolic spaces**C**: More interestingly, among ... | CAB | BCA | ACB | BCA | Selection 1 |
**A**:
In Task 2, Deciding About (Ir-)Relevant Sizes of Clusters, the goal was to determine the relative density (or, conversely, the sparsity) of the clusters**B**: The expected answer—see the visualization in Figure 6(c), for example—is that the benign cluster is denser (even though it may appear less dense, when no... | CBA | CBA | ACB | ABC | Selection 4 |
**A**: Considering the previous examples, it is clear that the real behavior of the algorithm is much more informative than its natural or biological inspiration**B**: This observation is in accordance with previous works in the literature, which have put to question whether the novelty in the natural inspiration of th... | BCA | ACB | CBA | BCA | Selection 2 |
**A**: GAEs proposed in [20, 29, 22] intend to reconstruct the adjacency via decoder while GAEs developed in [21] attempt to reconstruct the content**B**: The difference is which extra mechanism (such as attention, adversarial learning, graph sharpness, etc.) is used.**C**: To apply graph convolution on unsupervised le... | CBA | BCA | CBA | CBA | Selection 2 |
**A**: At the probing phase the counter’s value will equal or large than the expected value after the increment phase. The repeated measurements ensure that we do not accidentally interpret noise (i.e., packets from other sources to the same server) as lack of ingress filtering.**B**: When spoofing is not filtered the ... | CAB | CBA | ACB | BCA | Selection 2 |
**A**:
Second, skill NN and context+skill NN models were compared**B**: When added to the feedforward NN representation, such contextual information resulted in improved ability to compensate for sensor drift. This benefit was larger in later batches where the drift was the largest and where there was a longer context... | ABC | ACB | BCA | BCA | Selection 2 |
**A**: The version for automaton semigroups does not follow directly from 8, as the free monogenic semigroup is not a complete automaton semigroup [4, Proposition 4.3] or even a (partial) automaton semigroup (see [8, Theorem 18] or [20, Theorem 1.2.1.4]).
**B**: As an example, we prove in the following theorem that it ... | CAB | BAC | BAC | CBA | Selection 4 |
**A**: (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_POSTSUPERSCRIPT - 5 end_POSTSUPERSCRIPT and the weight for the HINT loss is set to 2222.
**B**: (2019), we train HINT on the ... | ABC | CBA | CAB | ABC | Selection 2 |
**A**: After manual comparison of a number of content extraction tools, we used the open-source Python package boilerpipe (Kohlschütter et al., 2010) due to its superior performance. Boilerpipe effectively strips web pages of boilerplate using shallow text features, structural features and density based features.**B**:... | CBA | ACB | CAB | ACB | Selection 1 |
**A**: For our example in Figure 4(a), they are enabled.**B**: These last two approaches are very resource-intensive; therefore, they can be turned off for larger data sets (by disabling Detailed Feature Search in StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Me... | BCA | CAB | BAC | CBA | Selection 4 |
**A**: In Weibo, FewRel and Amazon, the percentages that MAML outperforms the baselines by also decrease as the data quantity increasing. This finding is in line with the mechanism of MAML. MAML finds a sensitive parameter initialization that can adapt with few data samples [Finn et al., 2017].**B**: Data Quantity. In ... | CAB | CBA | BCA | BCA | Selection 1 |
**A**: UAV position-attitude prediction is performed to obtain the future motion state information (MSI) before next information feedback**B**:
Figure 3: The considered CC-enabled UAV mmWave network consists of a r-UAV and multiple t-UAVs**C**: The CCA and the beam are shown in detail in the CCA view. | BCA | BAC | CBA | ABC | Selection 2 |
**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... | ABC | BCA | BAC | BCA | Selection 3 |
**A**: Although nonlinear gradient TD converges, it is unclear whether the attained solution is globally optimal**B**: On the other hand, when the value function approximator in TD is an overparameterized multi-layer neural network, which is required to be properly scaled, such a feature representation stabilizes at th... | BCA | CAB | BAC | CBA | Selection 1 |
**A**: (2019); Li et al. (2022a), and the use of wider models is the usual method of choice for further improvements. Although for the Base Transformer model our approach does not lead to significant improvements for models deeper than 18181818 layers, we argue that the 18-layer Transformer Base is not the performance ... | BAC | BAC | CBA | BCA | Selection 4 |
**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 | ABC | BAC | ABC | Selection 1 |
**A**: As we can observe, the farther the pixel is away from the principal point, the larger the distortion degree is, and vice versa. This prior knowledge enables the neural networks to build a clear cognition with respect to the distortion distribution. Thus, the learning model gains a sufficient distortion perceptio... | CBA | ACB | BAC | CAB | Selection 1 |
**A**: Furthermore, it achieves faster convergence rates than LARS for the small and large batch sizes, which is consistent with our convergence analysis for the block-wise update strategy.**B**: Figure 2 shows the learning curves of the five methods**C**: We can observe that in the small-batch training, SNGM and other... | CAB | CBA | ABC | CBA | Selection 1 |
**A**: Nathaniel Grammel and Leonidas Tsepenekas were supported in part by NSF awards CCF-1749864 and CCF-1918749, and by research awards from Amazon and Google**B**: Aravind Srinivasan was supported in part by NSF awards CCF-1422569, CCF-1749864 and CCF-1918749, and by research awards from Adobe, Amazon, and Google.
*... | BAC | CBA | BCA | CAB | Selection 3 |
**A**: That is, the mean square error at the next time can be controlled by that at the
previous time and the consensus error**B**: Also, different from [15], the subgradients are not required to be bounded and the inequality (28) in [15] does not hold.**C**: However, this can not be obtained for the case with the line... | BCA | CAB | BCA | ACB | Selection 4 |
**A**: Suppose that an adversary aims to find the record of Helen in the anonymized table by matching her age value of 28, and the anonymization process is hidden for the adversary**B**: According to Figure 3, there are three records, namely Daphne, Helen, and Dean, may carry 28. Therefore, the probability that Helen i... | ABC | CAB | ACB | ABC | Selection 3 |
**A**: Note that several attempts, like BFP Pang et al. (2019) and EnrichFeat, give no improvements against PointRend baseline, while they serve as final ensemble candidates**B**: In addition to models listed in Table 3, another PointRend with slightly different setting (stacking two BFP modules, and increasing the RoI... | CAB | CAB | BCA | CAB | Selection 3 |
**A**:
In version 1 of this note, which can still be found on the ArXiv, we showed that the analogous version of the conjecture for complex functions on {−1,1}nsuperscript11𝑛\{-1,1\}^{n}{ - 1 , 1 } start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT which have modulus 1111 fails**B**: This solves a question raised by ... | CBA | ABC | ACB | CAB | Selection 2 |
**A**:
Compared to OPT-WLSVI and MASTER, our proposed algorithms achieve comparable empirical performance. More specifically, MASTER outperforms our proposed algorithm which agrees with its dynamic regret upper bound**B**: However, the variance of MASTER is larger due to the random scheduling of multiple base algorith... | ABC | BAC | ACB | CBA | Selection 1 |
**A**: Many studies worldwide have observed the proliferation of fake news on social media and instant messaging apps, with social media being the more commonly studied medium. In Singapore, however, mitigation efforts on fake news in instant messaging apps may be more important**B**: These suggest that, in Singapore, ... | CAB | BCA | BCA | ACB | Selection 4 |
**A**: Drawing inspiration from the CBOW schema, we propose Decentralized Attention Network (DAN) to distribute the relational information of an entity exclusively over its neighbors.
DAN retains complete relational information and empowers the induction of embeddings for new entities**B**: In contrast, the existing me... | CBA | ABC | ACB | CBA | Selection 3 |
**A**: Before training, the agent interacts with the environments for 104superscript10410^{4}10 start_POSTSUPERSCRIPT 4 end_POSTSUPERSCRIPT steps to estimate the mean and standard deviation of the states. We further normalize the observed states for training by the estimated mean and standard deviation before training.... | BAC | CAB | ACB | BCA | Selection 2 |
**A**: In doing so, we revisit earlier results by Carl de Boor and Amon Ros [28, 29] and answer their question from our perspective.**B**: We complement the established notion of unisolvent nodes by the dual notion of unisolvence**C**: That is: For given arbitrary nodes P𝑃Pitalic_P, determine the polynomial space ΠΠ\P... | BAC | CAB | ABC | BCA | Selection 2 |
**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 | CBA | CBA | CAB | Selection 1 |
**A**: Optical logic aggregates can be designed in the same way as in Implementation of Structural Computer Using Mirrors and Translucent Mirrors, and for the convenience of expression and the exploration of mathematical properties (especially their association with matrices), the number shown in Fig. 5 can be applied ... | ACB | BAC | CBA | CBA | Selection 1 |
**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... | CBA | CAB | ABC | ABC | Selection 2 |
**A**: To assess classification performance, we generate a matching test set of 1000 observations for each training set, and calculate the classification accuracy of the stacked classifiers on this test set. To assess view selection performance we calculate three different measures: (1) the true positive rate (TPR), i.... | ACB | ABC | ACB | BAC | Selection 4 |
**A**: The red dot is the mean value, and the length of the red line on either side of the mean shows the standard deviation of the 25 results. The Wilcoxon rank-sum tests are pairwisely applied to the techniques, with the alternative hypothesis stating that the technique on the left is better than the one on the right... | CBA | ABC | BCA | BAC | Selection 1 |
**A**: [2010], Li et al**B**: [2017], Oh & Iyengar [2021] lead to loose prediction error upper bound. For this we first introduce a new notation:
**C**: 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 | BCA | CAB | CBA | CBA | Selection 1 |
**A**: Following FPN, some methods are proposed to further improve the architecture for higher efficiency and better accuracy, such as PANet [25], NAS-FPN [12], BiFPN [34]. Our proposed cross-scale graph pyramid (xGPN) adopts the idea of FPN and builds a pyramid of video features in the temporal domain instead of image... | CBA | BAC | ACB | CAB | Selection 1 |
**A**: The third expert (E3) is an ML engineer and manager in a large multinational company, working with recommendation systems. She has approximately 7.5 years of experience with ML, of which 2 years are associated with ensemble learning. The latter two experts have PhDs in computer science; none of our three experts... | BAC | BCA | ACB | CAB | Selection 4 |
**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... | BAC | ABC | CBA | ABC | Selection 1 |
**A**: The objective function defines the cost for matching points on the first shape to points on the second shape. In shape matching, the costs are typically computed based on feature descriptors, such as the heat kernel signature [14], wave kernel signature [2], or SHOT [61].**B**: A simple mathematical formulation ... | ACB | BAC | BCA | CBA | Selection 4 |
**A**: Both graph classes are characterized very similarly in [18], and we extended the simpler characterization of path graphs in [1] to include directed path graphs as well; this result can be of interest itself**B**: Thus, now these two graph classes can be recognized in the same way both theoretically and algorithm... | BCA | ACB | ACB | CAB | Selection 1 |
**A**: The four datasets can be downloaded from
http://www-personal.umich.edu/~mejn/netdata/**B**: For the four datasets, the true labels are suggested by the original authors, and they are regarded as the “ground truth” to investigate the performances of Mixed-SLIM methods in this paper.**C**: In this section, four re... | BCA | ACB | ACB | CAB | Selection 1 |
**A**: 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**B**: (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. ... | CBA | ACB | ABC | BCA | Selection 2 |
**A**: Parameter sharing may help. However, each intersection has a different traffic pattern, and a simple shared policy hardly learns and acts optimally at all intersections**B**:
To learn effective decentralized policies, there are two main challenges. Firstly, it is impractical to learn an individual policy for ea... | CAB | BAC | CAB | CAB | Selection 2 |
**A**: In contrast, our algorithms exploit natural, and PAC-learnable predictions concerning the frequency at which item sizes occur in the input,
and our analysis incorporates the prediction error into the performance guarantee. As in other AI-motivated works on bin packing, namely (?, ?, ?, ?), we assume a discrete m... | ACB | CAB | CBA | BCA | Selection 2 |
**A**: Therefore LoCondA uses only the base’s data model during training, which increases the efficiency and applicability of our approach.**B**: This framework extends the existing base hypermodels (Spurek et al., 2020a, b) with an additional module designed for mesh generation that relies on a parametrization of loca... | CBA | CAB | ABC | ACB | Selection 1 |
**A**: This requires careful analysis in two aspects**B**: First, the Lagrange multipliers 𝐳,𝐬𝐳𝐬{\bf z},{\bf s}bold_z , bold_s are not constrained, while the convergence rate result for the classical Mirror-Prox algorithm [45] is proved for problems on compact sets. Second, we need to show that the updates can be o... | BAC | BCA | ACB | ACB | Selection 2 |
**A**: Among these classes we can find the strictly fundamental class.**B**:
Different classes of cycle bases can be considered**C**: In [6] the authors characterize them in terms of their corresponding cycle matrices and present a Venn diagram that shows their inclusion relations | CAB | BCA | CBA | BAC | Selection 1 |
**A**: of Patáková [35, Theorem 2.3] into:
**B**: One immediate application of Theorem 1.2 is the reduction of fractional Helly numbers**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 | BCA | ACB | CAB | Selection 4 |
**A**: Next, the tool can use automatic feature selection techniques to compare the new features with the original ones, using the same methods as in G2**B**: Finally, the tool should let users select the proper mathematical operation according to their prior experience and the visual feedback (T4).**C**: G4: Generatio... | ABC | ABC | BCA | ACB | Selection 3 |
**A**: This paper demonstrated a hierarchical contour control implementation for the increase of productivity in positioning systems**B**: This control framework requires tuning of multiple parameters associated with an extensive number of iterations. We propose a sample-efficient joint tuning algorithm, where the perf... | ABC | BCA | ACB | CAB | Selection 3 |
**A**: In addition, we posit that the commonly used benchmarks are not challenging enough to test generalization to realistic scenarios. For example CelebA and Colored MNIST, two of the most widely used benchmarks, contain a single bias variable to mitigate: gender and color respectively**B**: It is unclear how well me... | ABC | CBA | CBA | ACB | Selection 1 |
**A**: They use online learning to fine-tune their model with the calibration samples.
Some studies investigate the relation between the gaze points and the saliency maps [125, 126]. Chang et al. utilize saliency information to adapt the gaze cestimation algorithm to a new user without explicit calibration[144]**B**: S... | CAB | BAC | ABC | CBA | Selection 2 |
**A**: Besides, the Deep BoF approach uses a differentiable quantization scheme that enables simultaneous training of both the quantizer and the rest of the network, instead of using fixed quantization merely to minimize the model size passalis2017learning . It is worth stating that our proposed method doesn’t need to ... | CBA | ABC | ACB | CAB | Selection 1 |
**A**: Nevertheless, we are interested in implementing such validity conditions as uses of sized types as future work. Relatedly, cyclic termination proofs for separation logic programs can be automated [BBC08, TB20], although it is unclear how they could generalize to concurrent programs (in the setting of concurrent ... | CAB | ABC | BAC | CBA | Selection 4 |
**A**: Judge. The judge is a trusted entity who is only responsible for arbitration in the case of illegal redistribution, as in existing traitor tracing systems [10, 11, 12, 13, 14, 3]**B**: After receiving the owner’s request for arbitration, the judge makes a fair judgment based on the evidence provided by the owner... | CAB | ABC | CBA | BAC | Selection 2 |
**A**: The four model classes (A, B, C, D) are defined in Section 5.1.1**B**: The last two columns are average improvements of our proposed model GraphFM compared with corresponding base models (“+”: increase, “-”: decrease).**C**:
Table 2: Performance comparison of different methods on three datasets | BCA | CAB | CBA | BAC | Selection 1 |
**A**: [2022] when computing the step size according to the strategy from Pedregosa et al**B**:
Requiring access to a zeroth-order and domain oracle are mild assumptions, that were also implicitly assumed in one of the three FW-variants presented in Dvurechensky et al**C**: [2020]; see 5 in Algorithm 4. The remaining ... | CAB | BAC | ABC | CAB | Selection 2 |
**A**:
We prove Theorem 6.1 by constructing a framework that simulates our streaming algorithm**B**: That is the only algorithm which our framework does not simulate exactly.**C**: The most involved part is simulating Extend-Active-Paths, which is described in Section 6.7 | ABC | ACB | ABC | CBA | Selection 2 |
**A**: CPP can be applied to a general class of unbiased compression operators and achieves linear convergence for strongly convex and smooth objective functions.
Second, we consider a broadcast-like version of CPP (B-CPP) which also achieves linear convergence rate for strongly convex and smooth objective functions. B... | BAC | CBA | BCA | CAB | Selection 4 |
**A**: We compare our algorithms: type of sliding (Algorithm 1) and type of local method (Algorithm 3)**B**: To the best of our knowledge, this is the first work that compares these approaches in the scope of neural networks, as previous studies were limited to simpler methods, such as regression problems [31, 29]. Our... | ACB | BAC | ACB | BCA | Selection 4 |
**A**: The secret to the success of these methods seems to lie in (C)CEs ability to compress the search space of opponent policies to an expressive and non-exploitable subset**B**: PSRO has proved to be a formidable learning algorithm in two-player, constant-sum games, and JPSRO, with (C)CE MSs, is showing promising re... | ACB | ACB | BAC | ABC | Selection 3 |
**A**: Missing proofs from this section appear in Appendix C.**B**: Our goal is to bound the distribution error of a mechanism that responds to queries generated by an adaptive analyst.
This bound will be achieved via a triangle inequality, by bounding both the posterior accuracy and the Bayes stability (Definition 3.3... | BCA | CBA | BAC | ACB | Selection 2 |
**A**: We therefore propose the following novel research direction: to investigate how preprocessing algorithms can decrease the parameter value (and hence search space) of FPT algorithms, in a theoretically sound way. It is nontrivial to phrase meaningful formal questions in this direction**B**: Hence NP-hard problems... | BCA | ACB | CAB | CBA | Selection 2 |
**A**:
The second group of methods [183, 141, 205, 16, 191, 187] learn object-to-object mapping conditioned on the background information**B**: 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**C**:... | BAC | ABC | ACB | ACB | Selection 2 |
**A**: To this end, we have employed data mining tools to uncover the correlations between service and context data, and have utilized machine learning results to showcase the correlations among cities and tasks..
**B**: Our studies have confirmed the correlations among sub-datasets and have demonstrated that urban mod... | ACB | BAC | BAC | CBA | Selection 4 |
**A**: This issue was already highlighted in the appendix to gal2016dropout .
**B**: One of the consequences is that this model might suffer from the validity problems discussed in Section 3.1**C**: One can immediately expect that, analogous to general mean-variance estimators with a Gaussian prediction interval, this ... | BCA | BCA | CBA | ACB | Selection 3 |
**A**: Accordingly, we build two PTMs, one for MIDI scores and the other for MIDI performances and evaluate their performance respectively on the downstream tasks. While the MIDI-score version can be applied to a wider array of tasks involving those with or without performance-related information, the MIDI-performance ... | CAB | BAC | BAC | BAC | Selection 1 |
**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 | BAC | ACB | CAB | ABC | Selection 2 |
**A**: Particularly, we propose a DL-enabled semantic communication system for speech recognition, named DeepSC-SR, by learning and extracting the text-related semantic features from the readable speech signals, then recovering the text transcription at the receiver. The main contributions of this article can be summar... | ACB | ABC | BAC | CBA | Selection 4 |
**A**: Most 2D WSSS methods use image-level labels**B**: Then, segmentation networks are trained using the pseudo-pixel-level labels. Besides the image-level label, other kinds of weak labels like point supervision[14] and scribble supervision[15, 16] which is similar to the weak setting in this work. Points and scribb... | ACB | BCA | BAC | BAC | Selection 1 |
**A**: All the illustrated images are from the KITTI val set. Zoom in on the circles for more detailed comparison.**B**: We use black box for ground-truth, red box for baseline results, and blue box for our results**C**:
Figure 6: Qualitative results of our method for Bird’s-Eye-View | BCA | ABC | BCA | CBA | Selection 4 |
**A**: The GCN now has the ability to classify the type of segments, which will benefit the following FPNS step. In these figures, Non-Text Segments are not displayed for clarity.**B**: The Interval Segments and Char Segments are shown with different colors**C**:
Figure 5: The results of weakly supervised annotation (... | CAB | BAC | BCA | CBA | Selection 4 |
**A**: The first proposed mapping mechanism of IP addresses is TLMB. The four parts of the IP address are represented in four layers, where each layer is made up of one or more memory blocks. The first layer only contains one memory block, whereas the second layer contains 256 memory blocks. Each memory block contains ... | BCA | ABC | ACB | CBA | Selection 3 |
**A**: Generalizations to n𝑛nitalic_n-tuple cases are provided in Section 5. In Section 6, numerical experiments for a 3-field formulation of the Biot model are provided to justify the advantages of using positively stable preconditioners. Finally, concluding remarks are given in Section 7.
**B**: Furthermore, we exte... | CBA | BCA | ABC | BCA | Selection 1 |
**A**: The authors in works (Chen et al., 2020; Hardy et al., 2017; Yang et al., 2019b; Wu et al., 2020; Feng and Yu, 2020; Kang et al., 2020) propose vertical federated learning algorithms for single-tier communication networks, but they do not use local iterations in the parties during training**B**: Several works ha... | ACB | CAB | CAB | BAC | Selection 4 |
**A**: The generalization of eigenvalues from matrices to tensors has been studied through the implementation of tensor-tensor multiplication**B**: In Lund2020 , Lund defined a tensor eigendecomposition for third-order tensors with diagonalizable faces.**C**: Significant attention and extensive research have been devot... | CAB | ABC | ACB | CAB | Selection 3 |
**A**: In this way, the two parallel-coupled streams are individually modeled and combined to complement each other**B**: Correspondingly, a two-branch discriminator is developed to estimate the performance of this generation, which supervises the model to synthesize realistic pixels and sharp edges simultaneously for ... | ABC | BAC | BCA | BAC | Selection 3 |
**A**: In a binary erasure channel (BEC), a binary symbol is either received correctly or totally erased with probability ε𝜀\varepsilonitalic_ε**B**: The concept of BEC was first introduced by Elias in 1955 InfThe **C**: Together with the binary symmetric channel (BSC), they are frequently used in coding theory and in... | ABC | BCA | BAC | ACB | Selection 1 |
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