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**A**: For example, special linear groups are generated by the subset of all transvections [21, Theorem 4.3] or by two well chosen matrices, such as the Steinberg generators [19]**B**:
There are several well-known generating sets for classical groups**C**: Another generating set which has become important in algorithm... | CBA | CBA | BAC | BCA | Selection 3 |
**A**: We note that the idea of performing global static condensation goes back to the Variational Multiscale Finite Element Method–VMS [MR1660141, MR2300286]. Recently variations of the VMS**B**:
It is essential for the performing method that the static condensation is done efficiently**C**: The solutions of (22) dec... | ABC | BCA | BCA | CAB | 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... | CAB | ABC | CAB | CBA | Selection 4 |
**A**: Inspired by [33], we combine CNN and RNN into a unified model for tweet representation and classification. The model utilizes CNN to extract a sequence of higher-level phrase representations, which are fed into a long short-term memory (LSTM) RNN to obtain the tweet representation. This model, called CNN+RNN hen... | BAC | ABC | CBA | CAB | Selection 3 |
**A**: However, if we initialize with η<1/ℒ(𝐰(0))𝜂1ℒ𝐰0\eta<1/\mathcal{L}(\mathbf{w}(0))italic_η < 1 / caligraphic_L ( bold_w ( 0 ) ) then it is straightforward to show the gradient descent iterates maintain bounded local smoothness**B**: and probit losses.
Assumption 1 implies**C**: Assumption 1 includes many comm... | ACB | CAB | BCA | ABC | Selection 3 |
**A**: Our system already achieves 87% accuracy in 25 hours. We illustrate two examples here in Figures 12(a) and 12(b). Figure 12(a) is a rumor about ‘Okra curing diabetes’ 161616http://www.snopes.com/medical/homecure/okra.asp which we detected the beginning time is 01.31.2014 04:00**B**: Snope debunked it at 01.28.20... | ACB | BCA | ABC | ABC | Selection 2 |
**A**:
We propose two sets of features, namely, (1) salience features (taking into account the general importance of candidate aspects) that mainly mined from Wikipedia and (2) short-term interest features (capturing a trend or timely change) that mined from the query logs**B**: In addition, we also leverage click-flo... | BCA | ABC | CAB | CBA | Selection 2 |
**A**:
Table 1 shows basic patient information. Half of the patients are female and ages range from 17 to 66, with a mean age of 41.8 years**B**: Body weight, according to BMI, is normal for half of the patients, four are overweight and one is obese. The mean BMI value is 26.9**C**: Only one of the patients suffers fr... | ABC | BCA | CAB | CAB | Selection 1 |
**A**: Related approaches also focused on the potential benefits of incorporating activation from both coarse and fine image resolutions Huang et al. (2015), and recurrent connections to capture long-range spatial dependencies in convolutional feature maps Cornia et al. (2018); Liu and Han (2018). Our model explicitly ... | CBA | ABC | ACB | ACB | Selection 1 |
**A**: Consequently, approximating MinLoc within any constant factor is also SSE-hard. In particular, we point out that stronger inapproximability results for MinCutwidth are not known.
**B**: Before presenting the main results of this section, let us briefly discuss some inapproximability results for MinLoc that direc... | ACB | ABC | CBA | CAB | Selection 4 |
**A**: Random starts**B**: 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.
**C*... | BCA | ACB | ABC | BAC | Selection 2 |
**A**: In order to account for the robot’s dynamics and precisely quantify energy consumption during step negotiation, we utilized the Vortex physical engine incorporated within CoppeliaSim (previously known as V-REP) robotics simulation software [25]**B**: Our choice of CoppeliaSim as the robot modeling and simulation... | ACB | CBA | BAC | CAB | Selection 1 |
**A**:
All the above results pertain to deterministic online algorithms**B**: First, we show that the randomized algorithm of Purohit et al. [29] for the ski rental problem Pareto-dominates any deterministic algorithm, even when the latter is allowed unbounded advice.**C**: In Section 6, we study the power of randomiz... | CBA | ACB | BAC | ABC | Selection 2 |
**A**: those caused by not using other information than text for classification, another limitation in the present work is that we used words as the basic building blocks (i.e**B**: Besides the limitations described in Subsection 5.2, e.g**C**: each writing was processed as a Bag of Words) on which our approach begins ... | CBA | BAC | BCA | ABC | Selection 2 |
**A**: Note that the convergence guarantee of DEF-A and its momentum variant for non-convex problems is lacking in (Xu and Huang, 2022)**B**: We eliminate the assumption of ring-allreduce compatibility from (Xu and Huang, 2022) and only assume that the compressor has the δ𝛿\deltaitalic_δ-approximate property. This mak... | BCA | ACB | ABC | ABC | Selection 2 |
**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 | CBA | BCA | CAB | CAB | Selection 1 |
**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... | ACB | BCA | ABC | BCA | Selection 3 |
**A**: , caligraphic_O ( italic_h start_POSTSUBSCRIPT italic_e end_POSTSUBSCRIPT )) when applied to nonlinear**B**: italic_e **C**: functions with linear dependence on r𝑟ritalic_r and z𝑧zitalic_z, and have first
order accuracy (i.e.,𝒪(he)i.e.,\,\mathcal{O}(h_{e})italic_i | ABC | BAC | CBA | CAB | Selection 3 |
**A**: It was composed of two hidden layers of 128 neurons and two Dropout layers between the input layer and the first hidden layer and between the two hidden layers**B**:
A fully connected neural network architecture was used**C**: ADAM optimizer for the minimization[25]. | BCA | ABC | BCA | BAC | Selection 4 |
**A**:
Going beyond pixel intensity-based scene understanding by incorporating prior knowledge, which have been an active area of research for the past several decades (Nosrati and Hamarneh, 2016; Xie et al., 2020)**B**: Encoding prior knowledge in medical image analysis models is generally more possible as compared t... | BAC | CBA | ABC | BAC | Selection 3 |
**A**: Afterward, the number of training examples is limited to nlimitsubscript𝑛limitn_{\text{limit}}italic_n start_POSTSUBSCRIPT limit end_POSTSUBSCRIPT examples per class. We evaluate the training with 5555, 10101010, 20202020, and 50505050 examples per class.
In contrast to Fernández-Delgado et al**B**: (2014), we ... | ACB | CBA | ABC | BCA | Selection 4 |
**A**: As a result, such a lack of statistical understanding hinders the development of more sample-efficient policy optimization algorithms beyond heuristics. In fact, empirically, vanilla policy gradient is known to exhibit a possibly worse sample complexity than random search (Mania et al., 2018), even in basic sett... | BCA | ACB | ACB | CAB | Selection 4 |
**A**: (2019a) and Cai et al. (2019).
They introduce gates for every layer that determine the number of bits used for quantization, and they perform continuous stochastic optimization of probability parameters associated with each of these gates.**B**: (2018a) performed mixed-precision quantization using similar NAS co... | CAB | ACB | CBA | BCA | Selection 3 |
**A**: Despite the existence of a complete answer to the question for the case of 𝕊1superscript𝕊1\mathbb{S}^{1}blackboard_S start_POSTSUPERSCRIPT 1 end_POSTSUPERSCRIPT [4] due to Adams and Adamaszek, relatively little is known for higher dimensional spheres**B**: In [5] the authors consider a variant of the Vietoris-... | CBA | BCA | BAC | ABC | Selection 2 |
**A**: In t-viSNE, we focus on bringing forward hidden information about the DR algorithm that is usually lost, with all the interactions occurring in a single main scatterplot view (and some additional auxiliary views)**B**: One of our goals is to also support the user in testing the quality of the algorithm to increa... | BAC | BCA | CAB | ACB | Selection 2 |
**A**: We perform a brief review of recent studies that address good practices for designing metaheuristics and discussions from this perspective, and a short review of references – without attempting to be exhaustive – that address taxonomies, overviews, and general approaches in bio-inspired optimization**B**:
Lastl... | BCA | BCA | BAC | ABC | Selection 3 |
**A**: 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 that the graph is constructed by an algorithm rather than prior information. If the graph is not updated, the contained information is low-lev... | BCA | ABC | BAC | CAB | Selection 1 |
**A**: **B**: 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**C**:
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... | CBA | ABC | ABC | ACB | Selection 1 |
**A**: The context+skill NN model builds on the skill NN model by adding a recurrent processing pathway (Fig. 2D)**B**: Before classifying an unlabeled sample, the recurrent pathway processes a sequence of labeled samples from the preceding batches to generate a context representation, which is fed into the skill proce... | ACB | CAB | BCA | ABC | Selection 4 |
**A**: For semigroups, on the other hand, such results do exist**B**: However, there do not seem to be constructions for presenting arbitrary free products of self-similar groups in a self-similar way**C**: In fact, the free product of two automaton semigroups S𝑆Sitalic_S and T𝑇Titalic_T is always at least
very close... | ABC | BCA | BCA | BAC | Selection 4 |
**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**... | CAB | ABC | BAC | ABC | Selection 3 |
**A**: We fine-tuned PrivBERT on the training set as a binary classification task on each question-answer sentence pair to identify if the sentence is evidence for the question or not. We trained the model with a dropout of 0.2 and a learning rate of 3e-6 with the positive and negative classes weighted in the ratio 8:1... | ABC | CAB | BCA | CBA | Selection 4 |
**A**: Optimized Models for Specific Predictions**B**: Some models perform well according to our metrics, but others could be removed due to lower performance. However, we should try not to break the balance between performance and diversity of our stacking ensemble.
Thus, we choose to remove some of the models that ar... | ACB | CBA | CBA | ABC | Selection 1 |
**A**: We use PPL and BLEU [Papineni et al., 2002] to measure the similarity between the reference and the generated response, and use C Score [Madotto et al., 2019] to measure the personality**B**: In Persona we use a pre-trained natural language inference model to measure the response consistency with persona descrip... | BCA | CAB | ACB | CAB | 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 | CBA | BAC | CBA | Selection 3 |
**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**: We**C**: This will be boo... | BCA | BAC | ABC | CAB | Selection 1 |
**A**: Meanwhile, our analysis is related to the recent breakthrough in the mean-field analysis of stochastic gradient descent (SGD) for the supervised learning of an overparameterized two-layer neural network (Chizat and Bach, 2018b; Mei et al., 2018, 2019; Javanmard et al., 2019; Wei et al., 2019; Fang et al., 2019a,... | BCA | ACB | CAB | CBA | Selection 2 |
**A**: In a sense, the layer-by-layer computations in Transformer encoder and decoder stacks are just such sequences where information from a Transformer layer n−1𝑛1n-1italic_n - 1 is passed on to layer n𝑛nitalic_n. Our depth-wise LSTMs connect layers of multi-head attention information instead of token embeddings. B... | ABC | CAB | BAC | ACB | Selection 2 |
**A**: We claim that V1×W⊆f−1(U)subscript𝑉1𝑊superscript𝑓1𝑈V_{1}\times W\subseteq f^{-1}(U)italic_V start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT × italic_W ⊆ italic_f start_POSTSUPERSCRIPT - 1 end_POSTSUPERSCRIPT ( italic_U ). To prove this fact notice that**B**: We define W≜⋂x′∈FV2(x′,y′)≜𝑊subscriptsuperscript𝑥′𝐹sup... | ABC | ABC | CAB | CBA | Selection 4 |
**A**: In this section, we first state the details of the synthetic distorted image dataset and the training process of our learning model**B**: Subsequently, we analyze the learning representation for distortion estimation**C**: To demonstrate the effectiveness of each module in our framework, we conduct an ablation s... | BAC | ABC | CBA | CBA | Selection 2 |
**A**: Figure 2 shows the learning curves of the five methods**B**: 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.**C**: We can observe that in the small-batch training, SNGM and other... | CBA | ACB | ABC | CBA | Selection 2 |
**A**:
Unfortunately, standard SAA approaches [26, 7] do not directly apply to radius minimization problems**B**: See Appendix A for an in-depth discussion.**C**: On a high level, the obstacle is that radius-minimization requires estimating the cost of each approximate solution; counter-intuitively, this may be harder... | CBA | BAC | ACB | CBA | Selection 3 |
**A**:
II**B**: The sequence of random digraphs is conditionally balanced, and the weighted adjacency matrices are not required to have special statistical properties such as independency with identical distribution, Markovian switching, or stationarity, etc. The edge weights are also not required to be nonnegative at... | CAB | CAB | ACB | BAC | Selection 3 |
**A**: Specifically, there are three main steps in the proposed approach. First, MuCo partitions the tuples into groups and assigns similar records into the same group as far as possible. Second, the random output tables, which control the distribution of random output values within each group, are calculated to make s... | CBA | CAB | BAC | ACB | Selection 4 |
**A**: In addition to models listed in Table 3, another PointRend with slightly different setting (stacking two BFP modules, and increasing the RoIAlign size from original 7 to 10 for bounding box branch) is trained and achieves 76.95 mAP on testing set. So, there are 5 models used for final ensemble.
**B**: As shown i... | ACB | BAC | CAB | CBA | 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 ... | BCA | CBA | CAB | ABC | Selection 4 |
**A**: The environment changes abruptly in the left subfigure, whereas the environment changes gradually in the right subfigure.**B**: The results are averaged over 10 trials and the error bars show the standard deviations**C**:
Figure 1: Comparisons of different methods on cumulative reward under two different enviro... | ABC | BAC | CBA | CAB | Selection 3 |
**A**:
In this study, we seek to answer these research questions. RQ1: How much do people trust the media by which they obtain news? RQ2: Why do people share news and how do they do it? RQ3: How do people view the fake news phenomenon and what measures do they take against it? An online survey was employed for data co... | BCA | ACB | BCA | ABC | Selection 4 |
**A**: While on the FB15K-237 and WN18RR datasets, the performance of decentRL is slightly below the best-performing methods, it does achieve the best Hits@10 on FB15K-237. It is worth noting that FB15K-237 and WN18RR pose greater challenges for methods not tailored to this task, such as RSN [34] and decentRL.**B**:
T... | BCA | CBA | CAB | CBA | Selection 3 |
**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**:
One rea... | CAB | BCA | CBA | ACB | Selection 1 |
**A**: The polynomial convergence rates of Floater-Hormann and all**B**: The observations made in 2D remain valid**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 | BAC | ABC | BCA | Selection 1 |
**A**: For example, in Figure 1, the model uses β𝛽\betaitalic_β-TCVAE [mig] to retrieve the pose of the model as a latent factor. In the reconstruction, the rest of the details are averaged, resulting in a blurry image (1b). The goal of the second part of the model, is to add the details while maintaining the semantic... | CAB | CBA | CAB | ACB | Selection 2 |
**A**: 4 is an implementation of an XOR gate combining NAND and OR, expressed in 33 vertices and 46 mains**B**: Graphs are expressed in red and blue numbers in cases where there is no direction of the main line (the main line that can be passed in both directions) and the direction of the main line (the main line that... | ACB | BCA | CAB | ACB | Selection 2 |
**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... | CBA | BAC | BCA | CAB | Selection 2 |
**A**: We apply multi-view stacking to each simulated training set, using logistic ridge regression as the base-learner. Once we obtain the matrix of cross-validated predictions 𝒁𝒁\bm{Z}bold_italic_Z, we apply the seven different meta-learners**B**: the average proportion of views truly related to the outcome that we... | ACB | CBA | BAC | ABC | Selection 1 |
**A**: MI, IEPC and DA are non-causal feature selection methods. MI is a mutual information-based feature selection method. IEPC and DC are consistency-based and dependency-based methods, respectively**B**: For Phase 1, five feature selection methods, including 2 causal and 3 non-causal methods, are used in our experim... | BCA | CAB | ABC | BAC | Selection 4 |
**A**: [2020] to a multinomial problem, their setting is materially different from ours**B**:
Comparison with Amani & Thrampoulidis [2021] While the authors in Amani & Thrampoulidis [2021] also extend the algorithms of Faury et al**C**: They model various click-types for the same advertisement (action) via the multino... | BAC | ACB | ABC | ABC | Selection 1 |
**A**: Please note that in addition to using VSS to generate multi-scale input, we also directly use all original long videos as input in order to detect long actions as well.
**B**: As illustrated in Fig. 3, it takes a video sequence, extracts snippet-level features, cuts into multiple short clips if it is long, up-sc... | BCA | CBA | CAB | ABC | Selection 2 |
**A**: Li et al. [LCW∗18] found that once the ML expert has acquired all the results from an execution stage, he/she should analyze them with various perspectives and decide if the previously explored models’ performance match his/her needs**B**: This entire process should be trackable and manageable from the user’s si... | BCA | BCA | ACB | BAC | Selection 3 |
**A**: In many scenarios, the network topology of the consensus protocol is a switching topology due to failures, formation reconfiguration, or state-dependence**B**: There is a large number of papers that propose consensus protocols with switching network topologies and convergence proofs of these algorithms are provi... | CBA | BCA | CBA | ABC | Selection 2 |
**A**: As such, matching shape A via shape B to shape C, may lead to a different correspondence than matching shape A directly to C.
**B**: Alternatively, one could solve pairwise shape matching problems between all pairs of shapes in the shape collection**C**: Although this way there is no bias, in general the resulti... | ACB | CAB | BCA | ACB | Selection 2 |
**A**: A graph is a chordal graph if it does not contain a hole as an induced subgraph, where a hole is a chordless cycle of length at least four**B**: Gavril [8] proves that a graph is chordal if and only if it is the intersection graph of subtrees of a tree. We can recognize chordal graphs in O(m+n)𝑂𝑚𝑛O(m+n)itali... | CAB | BAC | BCA | ABC | Selection 3 |
**A**: The numerical results suggest that Mixed-SLIM methods enjoy satisfactory performances compared with SCORE, SLIM, OCCAM, Mixed-SCORE, and GeoNMF when detecting the four empirical datasets**B**: Table 2 records the error rates on the four real-world networks**C**: Especially, the number error for Mixed-SLIM on the... | ACB | ACB | BAC | ACB | Selection 3 |
**A**: (2016); Chen et al. (2016); Dalalyan (2017); Chen et al. (2017); Raginsky et al. (2017); Brosse et al. (2018); Xu et al**B**: See, e.g., Welling and Teh (2011); Chen et al. (2014); Ma et al. (2015); Chen et al. (2015); Dubey et al. (2016); Vollmer et al**C**: (2018); Cheng and Bartlett (2018); Chatterji et al. (... | CAB | CAB | CBA | BAC | Selection 4 |
**A**:
Mixedh**B**: The mixedh is a mixed high traffic flow with a total flow of 4770 in one hour, in order to simulate a heavy peak**C**: The difference from the mixedl setting is that the arrival rate of vehicles during 1200-1800s increased from 0.33 vehicles/s to 4.0 vehicles/s. The data statistics are listed in Ta... | CAB | BAC | CAB | ABC | Selection 4 |
**A**: In terms of the experimental analysis, in our experiments, the prediction error is a natural byproduct of the learning phase, and predictions are obtained by observing a small prefix of the input sequence. This is in contrast to several works in learning-enhanced algorithms, in which a perfect prediction is firs... | CAB | CBA | BAC | BAC | Selection 1 |
**A**: Therefore LoCondA uses only the base’s data model during training, which increases the efficiency and applicability of our approach.**B**: To that end, we propose a novel framework, LoCondA, capable of generating and reconstructing high-quality 3D meshes**C**: This framework extends the existing base hypermodels... | ACB | CAB | BAC | CBA | Selection 2 |
**A**: [37]).**B**: the dependence on the desired accuracy ε𝜀\varepsilonitalic_ε and condition number χ𝜒\chiitalic_χ of communication network if we split communication and oracle complexities by Chebyshev acceleration trick (see, e.g**C**: The Mirror-prox algorithm can be performed in a decentralized manner, however,... | ABC | CAB | CBA | ACB | Selection 3 |
**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 | BAC | BAC | BCA | CAB | Selection 4 |
**A**: 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.**B**: This technique, which we briefly outline here, was specifically designed for complete intersection patterns**C**:
The proof of Theorem 2.1 is quite in... | ABC | ACB | BAC | CBA | Selection 4 |
**A**: Feature transformation usually denotes less sophisticated modifications over the features [14]. Some of the standard transformations also supported by our approach are: (1) rounding, (2) binning, (3) scaling, (4) logarithmic transformations, (5) exponential transformations, and (6) power functions**B**: Unfortun... | CAB | BCA | BAC | ACB | Selection 4 |
**A**: We have implemented the simulations in Matlab, using Yalmip/Gurobi to solve the corresponding MPCC quadratic program in a receding horizon fashion and the GPML library for Gaussian process modeling**B**: We compare three schemes: manual tuning of the MPCC parameters for fixed low level controller gains, Tuning o... | BCA | BAC | ACB | CAB | Selection 1 |
**A**: To control the tuning distribution, we define a generalization of the mean per group accuracy (MPG) metric, that can interpolate within as well as extrapolate beyond the train and test distributions:
**B**: Assuming access to the test distribution for model selection is unrealistic and can result in models being... | CAB | BCA | ABC | BCA | Selection 1 |
**A**: They use an encoder extract appearance and gaze feature from eye images. They exchange the two features of selected paired images and aim to reconstruct the original image based on the exchanged feature.
Note that, these approaches learn the gaze representation, but they also require a few labeled samples to fin... | BAC | BCA | BCA | CBA | 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... | CBA | ABC | ACB | ACB | Selection 1 |
**A**: To review SAX, let us make observations about proof-theoretic polarity**B**: In the sequent calculus, inference rules are either invertible—can be applied at any point in the proof search process, like the right rule for implication—or noninvertible, which can only be applied when the sequent “contains enough in... | BAC | BAC | BCA | ABC | Selection 4 |
**A**: To this end, users request authorization from the owner, for example by paying for purchases. If successful, users can get the desired shared media content from the cloud. Users require that the plaintext of their fingerprints not be accessed by the owner or the cloud, to prevent malicious framing by the owner.
... | CBA | BCA | CBA | CAB | Selection 4 |
**A**: For example, in the context of predicting movie preferences, the feature interactions between Language and Release Date might not be relevant and, therefore, not provide useful information for prediction. Ignoring these irrelevant feature interactions can improve model training. To solve this problem, the Attent... | CAB | BCA | BAC | CAB | Selection 3 |
**A**: More specifically, the minimum of the Frank-Wolfe gap over the run of the algorithm converges at a rate of 𝒪(1/t)𝒪1𝑡\mathcal{O}(1/t)caligraphic_O ( 1 / italic_t )**B**:
Furthermore, with this simple step size we can also prove a convergence rate for the Frank-Wolfe gap, as shown in Theorem 2.6**C**: The ide... | BAC | ACB | ACB | CBA | Selection 1 |
**A**:
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 semi-streaming model [FKM+04], at least for sufficiently dense graphs**B**: We call an algorithm an α𝛼\alphaitalic_α-approximation if the matching has ... | ACB | CBA | BAC | CBA | Selection 1 |
**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**: CPP enjoys large flexibility in ... | CAB | CBA | ACB | ABC | Selection 4 |
**A**: It furthermore covers various communication topologies and hence goes beyond the centralized setting.
**B**: This extends the classical PFL problem to a broader class of problems beyond the classical minimization problem**C**: We present a new SPP formulation of the PFL problem (1) as the decentralized min-max m... | BCA | CBA | BCA | ACB | Selection 2 |
**A**: When evaluating, we measure equilibrium gaps under their own MS distribution and MW(C)CE to provide a consistent and value maximizing comparison. Experiments were ran for up to 6 hours, after which they were terminated.**B**:
We compare against common MS including uniform, α𝛼\alphaitalic_α-Rank (Omidshafiei et... | CAB | CBA | BAC | ABC | Selection 1 |
**A**: 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)**B**: In this section, we give a clean, new characterizatio... | ABC | ABC | BAC | CBA | Selection 3 |
**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**: To illustrate this dif... | ABC | BCA | ACB | BAC | Selection 1 |
**A**: [145] score the correlation between the distributions of predicted boxes and ground-truth boxes. Zhang et al**B**: To evaluate the quality of generated composite images, previous object placement works usually adopt the following three schemes: 1) Some works measure the similarity between real images and composi... | ACB | ABC | BAC | ACB | Selection 3 |
**A**: By leveraging this transferable knowledge across domains with this multi-city, multi-task data, CityNet can help researcher alleviate the data scarcity problems that arise in newly-built or under-developed cities.
**B**: In the field of urban computing, it is highly probable that the knowledge required for diffe... | BAC | BAC | ACB | CBA | Selection 4 |
**A**: In essence this means that a Bernoulli prior with parameter p𝑝pitalic_p is placed over every weight. The idea behind this method was to reduce the “co-adaptation” behaviour of fully-connected networks, i.e., it makes it harder for the weights to work together to overfit the data**B**:
Dropout layers were initi... | ABC | CBA | BAC | ACB | Selection 3 |
**A**: Directing attention to the red circled region within the pianoroll representation, it is evident that the CNN baseline faces challenges in effectively distinguishing between melody and accompaniment, particularly when note pitches reside within the C4 to C5 range during the initial phase**B**: This is especially... | BCA | ABC | CBA | ABC | Selection 1 |
**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... | ABC | ABC | ABC | CBA | Selection 4 |
**A**: Learning (ICML), Pittsburgh, USA, Jun. 2006, pp. 369–376.**B**: Conf. Mach**C**:
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,” in Proc. 23rd Int | BAC | CBA | CAB | ABC | Selection 2 |
**A**:
We use the KPConv[4] segmentation model KPFCNN as our backbone network. The network is an encoder-decoder fully convolutional network with skip connections**B**: The encoder is composed by bottleneck ResNet blocks[47] with KP convolution layers. The decoder part is composed of the nearest upsampling layers with... | ACB | CBA | CBA | ABC | Selection 4 |
**A**: The detection performance can be remarkably improved from 11.84% to 70.91% in terms of the AP40subscriptAP40{\rm AP}_{40}roman_AP start_POSTSUBSCRIPT 40 end_POSTSUBSCRIPT under the moderate setting of car category on the KITTI val set (see Table 1), which suggests that the depth estimation is a critical performa... | CAB | ABC | ACB | CBA | Selection 1 |
**A**: ContourNet [11] tried to suppress non-text areas by considering text maps in horizontal and vertical directions, which effectively suppressed false positives**B**: However, their method still suffered from false negatives when both horizontal and vertical text maps had defects.**C**:
A second prior approach is ... | CBA | ACB | BAC | BCA | 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 | BCA | CAB | 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... | BCA | CBA | ABC | ACB | Selection 3 |
**A**: MIMIC-III:
We train an LSTM model using the MIMIC-III (Medical Information Mart for Intensive Care) dataset, (Johnson et al., 2016), which consists of anonymized information of patients admitted to critical care units in a hospital**B**: We follow the data processing steps from (Harutyunyan et al., 2019) to obta... | BCA | CBA | CBA | ABC | Selection 4 |
**A**: Changxin Mo acknowledges support from the National Natural Science Foundation of China (Grant No. 12201092), the Natural Science Foundation Project of CQ CSTC (Grant No. CSTB2022NSCQ-MSX0896), the Science and Technology Research Program of Chongqing Municipal Education Commission
(Grant No**B**: KJQN202200512), ... | BCA | CAB | ABC | BCA | Selection 3 |
**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... | ABC | CBA | BCA | ABC | Selection 2 |
**A**: In a binary erasure channel (BEC), a binary symbol is either received correctly or totally erased with probability ε𝜀\varepsilonitalic_ε**B**: 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 | CBA | Selection 1 |
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