context stringlengths 250 4.63k | A stringlengths 250 6.41k | B stringlengths 250 5.14k | C stringlengths 250 3.8k | D stringlengths 250 8.2k | label stringclasses 4
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that adds the results of 1+(n−m)/21𝑛𝑚21+(n-m)/21 + ( italic_n - italic_m ) / 2
Gaussian integrations for moments xD−1+n−2ssuperscript𝑥𝐷1𝑛2𝑠x^{D-1+n-2s}italic_x start_POSTSUPERSCRIPT italic_D - 1 + italic_n - 2 italic_s end_POSTSUPERSCRIPT. The disadvantage | {n,n^{\prime}}.∫ start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT 1 end_POSTSUPERSCRIPT italic_x start_POSTSUPERSCRIPT italic_D - 1 end_POSTSUPERSCRIPT italic_R start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_m end_POSTSUPERSCRIPT ( italic_x ) italic_R start_POSTSUBSCRIPT italic... |
Gaussian integration rules for integrals ∫01xD−1Rnm(x)f(x)𝑑xsuperscriptsubscript01superscript𝑥𝐷1superscriptsubscript𝑅𝑛𝑚𝑥𝑓𝑥differential-d𝑥\int_{0}^{1}x^{D-1}R_{n}^{m}(x)f(x)dx∫ start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT 1 end_POSTSUPERSCRIPT italic_x start_POSTSUPERSCRIPT italic_D - 1 ... | +x\left[D-1-(D+1)x^{2}\right]\frac{d}{dx}R_{n}^{m}(x).start_ROW start_CELL italic_x start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ( italic_x start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT - 1 ) divide start_ARG italic_d start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT end_ARG start_ARG italic_d italic_x start_POSTSUPERSCRIPT 2 e... | rules for
the lifted integrals ∫01xD−1[1+Rnm(x)]f(x)𝑑xsuperscriptsubscript01superscript𝑥𝐷1delimited-[]1superscriptsubscript𝑅𝑛𝑚𝑥𝑓𝑥differential-d𝑥\int_{0}^{1}x^{D-1}[1+R_{n}^{m}(x)]f(x)dx∫ start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT 1 end_POSTSUPERSCRIPT italic_x start_POSTSUPERSCRIPT ita... | D |
On the other hand, if the instruction Itsubscript𝐼𝑡I_{t}italic_I start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT was Show(A)Show𝐴\operatorname{Show}(A)roman_Show ( italic_A ) then Eval(S,M,s,t)Eval𝑆𝑀𝑠𝑡\operatorname{Eval}(S,M,s,t)roman_Eval ( italic_S , italic_M , italic_s , italic_t ) is
defined to be the list ... | This adds only one extra MSLP instruction, in order to form and store the element xv−1𝑥superscript𝑣1xv^{-1}italic_x italic_v start_POSTSUPERSCRIPT - 1 end_POSTSUPERSCRIPT needed in the conjugate on the right-hand side of (2) (this element can later be overwritten and so does not add to the overall maximum memory quo... | does not yield an upper bound for the memory requirement in a theoretical analysis.
Moreover, the result of SlotUsagePattern improves the memory usage but it is not necessarily optimized overall and, hence, the number of slots can still be greater than the number of slots of a carefully computed MSLP. It should also be... |
Instruction type (i) above simply copies an element already in memory to a different memory slot. These instructions can arguably be disregarded for the purpose of determining the length of an MSLP, because in a practical implementation they could be handled via relabelling. |
For the purposes of determining the cost of Taylor’s algorithm in terms of matrix operations, namely determining the length of an MSLP for the algorithm, we assume that the field elements −gicgrc−1subscript𝑔𝑖𝑐superscriptsubscript𝑔𝑟𝑐1-g_{ic}g_{rc}^{-1}- italic_g start_POSTSUBSCRIPT italic_i italic_c end_POSTSU... | C |
where Ω⊂ℝdΩsuperscriptℝ𝑑\Omega\subset\mathbb{R}^{d}roman_Ω ⊂ blackboard_R start_POSTSUPERSCRIPT italic_d end_POSTSUPERSCRIPT with d=2𝑑2d=2italic_d = 2 or 3333 for simplicity, and is an open bounded domain with polyhedral boundary ∂ΩΩ\partial\Omega∂ roman_Ω, the symmetric tensor 𝒜∈[L∞(Ω)]symd×d𝒜superscriptsubscrip... | In [MR2718268] is shown that the number of eigenvalues that are very large is related to the number of connected sub-regions on τ¯∪τ¯′¯𝜏superscript¯𝜏′\bar{\tau}\cup{\bar{\tau}}^{\prime}over¯ start_ARG italic_τ end_ARG ∪ over¯ start_ARG italic_τ end_ARG start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT with large coefficien... |
As in many multiscale methods previously considered, our starting point is the decomposition of the solution space into fine and coarse spaces that are adapted to the problem of interest. The exact definition of some basis functions requires solving global problems, but, based on decaying properties, only local comput... | One difficulty that hinders the development of efficient methods is the presence of high-contrast coefficients [MR3800035, MR2684351, MR2753343, MR3704855, MR3225627, MR2861254]. When LOD or VMS methods are considered, high-contrast coefficients might slow down the exponential decay of the solutions, making the method ... | It is hard to approximate such problem in its full generality using numerical methods, in particular because of the low regularity of the solution and its multiscale behavior. Most convergent proofs either assume extra regularity or special properties of the coefficients [AHPV, MR3050916, MR2306414, MR1286212, babuos85... | D |
On the contrary, we may need to use a function θ𝜃\thetaitalic_θ of variable (b,c)𝑏𝑐(b,c)( italic_b , italic_c ); see the description of 𝖪𝗂𝗅𝗅Fsubscript𝖪𝗂𝗅𝗅𝐹\mathsf{Kill}_{F}sansserif_Kill start_POSTSUBSCRIPT italic_F end_POSTSUBSCRIPT in subsection 3.1 for an example.
As such, the flow of Rotate-and-Kill is ... |
Alg-A has simpler primitives because (1) the candidate triangles considered in it have all corners lying on P𝑃Pitalic_P’s vertices and (2) searching the next candidate from a given one is much easier – the code length for this is 1:7 in Alg-A and in Alg-CM. | Comparing the description of the main part of Alg-A (the 7 lines in Algorithm 1) with that of Alg-CM (pages 9–10 of [8]),
Alg-A is conceptually simpler. Alg-CM is claimed “involved” by its authors as it contains complicated subroutines for handling many subcases. | We think Alg-A is better in almost every aspect. This is because it is essentially simpler.
Among other merits, Alg-A is much faster, because it has a smaller constant behind the asymptotic complexity O(n)𝑂𝑛O(n)italic_O ( italic_n ) than the others: |
Our experiment shows that the running time of Alg-A is roughly one eighth of the running time of Alg-K, or one tenth of the running time of Alg-CM. (Moreover, the number of iterations required by Alg-CM and Alg-K is roughly 4.67 times that of Alg-A.) | C |
We observe that at certain points in time, the volume of rumor-related tweets (for sub-events) in the event stream surges. This can lead to false positives for techniques that model events as the aggregation of all tweet contents; that is undesired at critical moments. We trade-off this by debunking at single tweet le... |
As observed in [19, 20], rumor features are very prone to change during an event’s development. In order to capture these temporal variabilities, we build upon the Dynamic Series-Time Structure (DSTS) model (time series for short) for feature vector representation proposed in [20]. We base our credibility feature on t... |
Most relevant for our work is the work presented in [20], where a time series model to capture the time-based variation of social-content features is used. We build upon the idea of their Series-Time Structure, when building our approach for early rumor detection with our extended dataset, and we provide a deep analys... |
In this work, we propose an effective cascaded rumor detection approach using deep neural networks at tweet level in the first stage and wisdom of the “machines”, together with a variety of other features in the second stage, in order to enhance rumor detection performance in the early phase of an event. The proposed ... | at an early stage. Our fully automatic, cascading rumor detection method follows
the idea on focusing on early rumor signals on text contents; which is the most reliable source before the rumors widely spread. Specifically, we learn a more complex representation of single tweets using Convolutional Neural Networks, tha... | C |
The convergence of the direction of gradient descent updates to the maximum L2subscript𝐿2L_{2}italic_L start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT margin solution, however is very slow compared to the convergence of training loss, which explains why it is worthwhile
continuing to optimize long after we have zero training ... | decreasing loss, as well as for multi-class classification with cross-entropy loss. Notably, even though the logistic loss and the exp-loss behave very different on non-separable problems, they exhibit the same behaviour for separable problems. This implies that the non-tail
part does not affect the bias. The bias is a... | Let ℓℓ\ellroman_ℓ be the logistic loss, and 𝒱𝒱\mathcal{V}caligraphic_V be an independent validation set, for which ∃𝐱∈𝒱𝐱𝒱\exists\mathbf{x}\in\mathcal{V}∃ bold_x ∈ caligraphic_V such that 𝐱⊤𝐰^<0superscript𝐱top^𝐰0\mathbf{x}^{\top}\hat{\mathbf{w}}<0bold_x start_POSTSUPERSCRIPT ⊤ end_POSTSUPERSCRIPT over^ start_... | The follow-up paper (Gunasekar et al., 2018) studied this same problem with exponential loss instead of squared loss. Under additional assumptions on the asymptotic convergence of update directions and gradient directions, they were able to relate the direction of gradient descent iterates on the factorized parameteriz... | We should not rely on plateauing of the training loss or on the loss (logistic or exp or cross-entropy) evaluated on a validation data, as measures to decide when to stop. Instead, we should look at the 00–1111 error on the validation dataset. We might improve the validation and test errors even when when the decrease ... | D |
To overcome this issue, we set up a threshold 72 hours. We only consider the first candidate within 72 hours before or after the beginning time of the event as timestamp of human confirming rumors. On average the human editors of Snopes need 25.49 hours to verify the rumors and post it. Our system already achieves 87% ... |
We observe that at certain points in time, the volume of rumor-related tweets (for sub-events) in the event stream surges. This can lead to false positives for techniques that model events as the aggregation of all tweet contents; that is undesired at critical moments. We trade-off this by debunking at single tweet le... | the idea on focusing on early rumor signals on text contents; which is the most reliable source before the rumors widely spread. Specifically, we learn a more complex representation of single tweets using Convolutional Neural Networks, that could capture more hidden meaningful signal than only enquiries to debunk rumor... |
At 18:22 CEST, the first tweet was posted. There might be some certain delay, as we retrieve only tweets in English and the very first tweets were probably in German. The tweet is ”Sadly, i think there’s something terrible happening in #Munich #Munchen. Another Active Shooter in a mall. #SMH”. | The time period of a rumor event is sometimes fuzzy and hard to define. One reason is a rumor may have been triggered for a long time and kept existing, but it did not attract public attention. However it can be triggered by other events after a uncertain time and suddenly spreads as a bursty event. E.g., a rumor999htt... | C |
\mathcal{C}_{k}|a,t)\sum\limits_{l=1}^{m}P(\mathcal{T}_{l}|a,t,\mathcal{C}_{k}%
)\hat{y_{a}},y_{a})sansserif_f start_POSTSUPERSCRIPT * end_POSTSUPERSCRIPT = roman_arg roman_min start_POSTSUBSCRIPT italic_f end_POSTSUBSCRIPT ∑ start_POSTSUBSCRIPT ∀ italic_a end_POSTSUBSCRIPT caligraphic_L ( ∑ start_POSTSUBSCRIPT italic_... | Learning a single model for ranking event entity aspects is not effective due to the dynamic nature of a real-world event driven by a great variety of multiple factors. We address two major factors that are assumed to have the most influence on the dynamics of events at aspect-level, i.e., time and event type. Thus, we... | We further investigate the identification of event time, that is learned on top of the event-type classification. For the gold labels, we gather from the studied times with regards to the event times that is previously mentioned. We compare the result of the cascaded model with non-cascaded logistic regression. The res... | For this part, we first focus on evaluating the performance of single L2R models that are learned from the pre-selected time (before, during and after) and types (Breaking and Anticipate) set of entity-bearing queries. This allows us to evaluate the feature performance i.e., salience and timeliness, with time and type ... | 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. We adapted the L2R RankSVM [12]. The goal of RankSVM is learning a linear... | D |
In this case, the agent must sequentially learn both the underlying dynamics (La,Σa;∀asubscript𝐿𝑎subscriptΣ𝑎for-all𝑎L_{a},\Sigma_{a};\forall aitalic_L start_POSTSUBSCRIPT italic_a end_POSTSUBSCRIPT , roman_Σ start_POSTSUBSCRIPT italic_a end_POSTSUBSCRIPT ; ∀ italic_a)
and the conditional reward function’s variance ... | We observe noticeable (almost linear) regret increases when the dynamics of the parameters swap the identity of the optimal arm.
However, SMC-based Thompson sampling and Bayes-UCB agents are able to learn the evolution of the dynamic latent parameters, | We now describe in detail how to use the SMC-based posterior random measure pM(θt+1,a|ℋ1:t)subscript𝑝𝑀conditionalsubscript𝜃𝑡1𝑎subscriptℋ:1𝑡p_{M}(\theta_{t+1,a}|\mathcal{H}_{1:t})italic_p start_POSTSUBSCRIPT italic_M end_POSTSUBSCRIPT ( italic_θ start_POSTSUBSCRIPT italic_t + 1 , italic_a end_POSTSUBSCRIPT | cali... | For the more interesting case of unknown parameters,
we marginalize parameters Lasubscript𝐿𝑎L_{a}italic_L start_POSTSUBSCRIPT italic_a end_POSTSUBSCRIPT and ΣasubscriptΣ𝑎\Sigma_{a}roman_Σ start_POSTSUBSCRIPT italic_a end_POSTSUBSCRIPT of the transition distributions | If the support of q(⋅)𝑞⋅q(\cdot)italic_q ( ⋅ ) includes the support of the distribution of interest p(⋅)𝑝⋅p(\cdot)italic_p ( ⋅ ), one computes the IS estimator of a test function based on the normalized weights w(m)superscript𝑤𝑚w^{(m)}italic_w start_POSTSUPERSCRIPT ( italic_m ) end_POSTSUPERSCRIPT,
| A |
Table 2 gives an overview of the number of different measurements that are available for each patient.111For patient 9, no data is available.
The study duration varies among the patients, ranging from 18 days, for patient 8, to 33 days, for patient 14. | These are also the patients who log glucose most often, 5 to 7 times per day on average compared to 2-4 times for the other patients.
For patients with 3-4 measurements per day (patients 8, 10, 11, 14, and 17) at least a part of the glucose measuremtents after the meals is within this range, while patient 12 has only t... | Median number of blood glucose measurements per day varies between 2 and 7. Similarly, insulin is used on average between 3 and 6 times per day.
In terms of physical activity, we measure the 10 minute intervals with at least 10 steps tracked by the google fit app. | Patient 17 has more rapid insulin applications than glucose measurements in the morning and particularly in the late evening.
For patient 15, rapid insulin again slightly exceeds the number of glucose measurements in the morning. Curiously, the number of glucose measurements match the number carbohydrate entries – it i... | Likewise, the daily number of measurements taken for carbohydrate intake, blood glucose level and insulin units vary across the patients.
The median number of carbohydrate log entries vary between 2 per day for patient 10 and 5 per day for patient 14. | D |
Our proposed encoder-decoder model clearly demonstrated competitive performance on two datasets towards visual saliency prediction. The ASPP module incorporated multi-scale information and global context based on semantic feature representations, which significantly improved the results both qualitatively and quantita... | To assess the predictive performance for eye tracking measurements, the MIT saliency benchmark Bylinskii et al. (2015) is commonly used to compare model results on two test datasets with respect to prior work. Final scores can then be submitted on a public leaderboard to allow fair model ranking on eight evaluation met... | Later attempts addressed that shortcoming by taking advantage of classification architectures pre-trained on the ImageNet database Deng et al. (2009). This choice was motivated by the finding that features extracted from CNNs generalize well to other visual tasks Donahue et al. (2014). Consequently, DeepGaze I Kümmerer... | Further improvements of benchmark results could potentially be achieved by a number of additions to the processing pipeline. Our model demonstrates a learned preference for predicting fixations in central regions of images, but we expect performance gains from modeling the central bias in scene viewing explicitly Kümme... | For related visual tasks such as semantic segmentation, information distributed over convolutional layers at different levels of the hierarchy can aid the preservation of fine spatial details Hariharan et al. (2015); Long et al. (2015). The prediction of fixation density maps does not require accurate class boundaries ... | C |
Finally, we have to show that in this pd-marking scheme, the maximum number of activeactive\operatorname{\texttt{active}}act positions is bounded by 2k+12𝑘12k+12 italic_k + 1. This is obviously true at step p1subscript𝑝1p_{1}italic_p start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT. Now let s𝑠sitalic_s with 1≤s≤|α|−11𝑠𝛼11... | j𝑗jitalic_j joins two blocks of size 1111: the number of activeactive\operatorname{\texttt{active}}act positions increases by 1111.
This is due to the fact that by setting j𝑗jitalic_j to activeactive\operatorname{\texttt{active}}act, we do not create any internal activeactive\operatorname{\texttt{active}}act position... | We first prove pw(Gα)≤2loc(α)pwsubscript𝐺𝛼2loc𝛼\operatorname{\textsf{pw}}(G_{\alpha})\leq 2\operatorname{\textsf{loc}}(\alpha)pathwidth ( italic_G start_POSTSUBSCRIPT italic_α end_POSTSUBSCRIPT ) ≤ 2 loc ( italic_α ). Intuitively speaking, we will translate the stages of a marking sequence σ𝜎\sigmaitalic_σ for α... | This completes the definition of the marking scheme. Figure 7 contains an example of how step ps+1subscript𝑝𝑠1p_{s+1}italic_p start_POSTSUBSCRIPT italic_s + 1 end_POSTSUBSCRIPT is obtained from step pssubscript𝑝𝑠p_{s}italic_p start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT. In this example, we first set extending po... |
In the first phase of the marking scheme, i. e., the phase where we only set extending positions to activeactive\operatorname{\texttt{active}}act, the following different situations can arise, whenever we set some position j𝑗jitalic_j to activeactive\operatorname{\texttt{active}}act (see Figure 7 for an illustration)... | D |
Zubair et al.[75] detected the R-peak using a non-linear transformation and formed a beat segment around it.
Then, they used the segments to train a three layer 1D CNN with variable learning rate depending on the mean square error and achieved better results than previous state-of-the-art. | In their article Kiranyaz et al.[77] trained patient-specific CNNs that can be used to classify long ECG data stream or for real-time ECG monitoring and early alert system on a wearable device.
The CNN consisted of three layers of an adaptive implementation of 1D convolution layers. | Taji et al.[91] trained a DBN to classify acceptable from unacceptable ECG segments to reduce the false alarm rate caused by poor quality ECG during AF detection.
Eight different levels of ECG quality are provided by contaminating ECG with motion artifact from the NSTDB for validation. | Another three models were trained using the signals as 1D.
The first model was a FNN with dropout, the second a three layer 1D CNN and the third a 2D CNN same as the first but trained with a stacked version of the signal (also trained with data augmentation). | Experiments by the authors showed that the three layer 1D CNN created better and more stable results.
In[101] the authors trained a network with an one convolutional layer with dropout followed by two RNNs to identify stress using short-term ECG data. | A |
Our predictive model has stochastic latent variables so it can be applied in highly stochastic environments. Studying such environments is an exciting direction for future work, as is the study of other ways in which the predictive neural network model could be used. Our approach uses the model as a learned simulator a... | The results in these figures are generated by averaging 5555 runs for each game.
The model-based agent is better than a random policy for all the games except Bank Heist. Interestingly, we observed that the best of the 5555 runs was often significantly better. For 6666 of the games, it exceeds the average human score (... | In our empirical evaluation, we find that SimPLe is significantly more sample-efficient than a highly tuned version of the state-of-the-art Rainbow algorithm (Hessel et al., 2018) on almost all games. In particular, in low data regime of 100100100100k samples, on more than half of the games, our method achieves a score... |
The primary evaluation in our experiments studies the sample efficiency of SimPLe, in comparison with state-of-the-art model-free deep RL methods in the literature. To that end, we compare with Rainbow (Hessel et al., 2018; Castro et al., 2018), which represents the state-of-the-art Q-learning method for Atari games, ... |
While SimPLe is able to learn more quickly than model-free methods, it does have limitations. First, the final scores are on the whole lower than the best state-of-the-art model-free methods. This can be improved with better dynamics models and, while generally common with model-based RL algorithms, suggests an import... | D |
However more work needs to be done for full replacing non-trainable S2Is, not only from the scope of achieving higher accuracy results but also increasing the interpretability of the model.
Another point of reference is that the combined models were trained from scratch based on the hypothesis that pretrained low level... | For the purposes of this paper and for easier future reference we define the term Signal2Image module (S2I) as any module placed after the raw signal input and before a ‘base model’ which is usually an established architecture for imaging problems.
An important property of a S2I is whether it consists of trainable para... | Future work could include testing this hypothesis by initializing a ‘base model’ using transfer learning or other initialization methods.
Moreover, trainable S2Is and 1D ‘base model’ variations could also be used for other physiological signals besides EEG such as Electrocardiography, Electromyography and Galvanic Skin... | This is achieved with the use of multilayer networks, that consist of million parameters [1], trained with backpropagation [2] on large amount of data.
Although deep learning is mainly used in biomedical images there is also a wide range of physiological signals, such as Electroencephalography (EEG), that are used for ... | However more work needs to be done for full replacing non-trainable S2Is, not only from the scope of achieving higher accuracy results but also increasing the interpretability of the model.
Another point of reference is that the combined models were trained from scratch based on the hypothesis that pretrained low level... | B |
Hybrid robots typically transition between locomotion modes either by “supervised autonomy” [11], where human operators make the switch decisions, or the autonomous locomotion mode transition approach, where robots autonomously swap the modes predicated on pre-set criteria [8]. However, the execution of supervised con... | There are two primary technical challenges in the wheel/track-legged robotics area [2]. First, there’s a need to ensure accurate motion control within both rolling and walking locomotion modes [5] and effectively handle the transitions between them [6]. Second, it’s essential to develop decision-making frameworks that ... | The Cricket robot, as referenced in [20], forms the basis of this study, being a fully autonomous track-legged quadruped robot. Its design specificity lies in embodying fully autonomous behaviors, and its locomotion system showcases a unique combination of four rotational joints in each leg, which can be seen in Fig. 3... |
Hybrid robots typically transition between locomotion modes either by “supervised autonomy” [11], where human operators make the switch decisions, or the autonomous locomotion mode transition approach, where robots autonomously swap the modes predicated on pre-set criteria [8]. However, the execution of supervised con... | A major obstacle in achieving seamless autonomous locomotion transition lies in the need for an efficient sensing methodology that can promptly and reliably evaluate the interaction between the robot and the terrain, referred to as terramechanics. These methods generally involve performing comprehensive on-site measure... | D |
For paid exchanges at the beginning of the phase, Tog incurs a cost that is less than m2superscript𝑚2m^{2}italic_m start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT. Before serving the last request σℓsubscript𝜎ℓ\sigma_{\ell}italic_σ start_POSTSUBSCRIPT roman_ℓ end_POSTSUBSCRIPT of the phase, the access cost of Tog is less ... |
In an ignoring phase, the cost of Tog for the phase is in the range (βm3,βm3(1+1/m2))𝛽superscript𝑚3𝛽superscript𝑚311superscript𝑚2(\beta m^{3},\beta m^{3}(1+1/m^{2}))( italic_β italic_m start_POSTSUPERSCRIPT 3 end_POSTSUPERSCRIPT , italic_β italic_m start_POSTSUPERSCRIPT 3 end_POSTSUPERSCRIPT ( 1 + 1 / italic_m ... |
The worst-case ratio between the costs of Tog and Mtf2 is maximized when the last phase is an ignoring phase. In this case, we have k𝑘kitalic_k trusting phases and k𝑘kitalic_k ignoring phases. The total cost of Mtf2 is at least km3+k(βm3/2−m2)=km3(1+β/2−1/m)𝑘superscript𝑚3𝑘𝛽superscript𝑚32superscript𝑚2𝑘sup... |
For a trusting phase, the cost of Tog is in the range (m3,m3(1+1/m+1/m2))superscript𝑚3superscript𝑚311𝑚1superscript𝑚2(m^{3},m^{3}(1+1/m+1/m^{2}))( italic_m start_POSTSUPERSCRIPT 3 end_POSTSUPERSCRIPT , italic_m start_POSTSUPERSCRIPT 3 end_POSTSUPERSCRIPT ( 1 + 1 / italic_m + 1 / italic_m start_POSTSUPERSCRIPT 2 en... | Similar arguments apply for an ignoring phase with the exception that the threshold is β⋅m2⋅𝛽superscript𝑚2\beta\cdot m^{2}italic_β ⋅ italic_m start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT and there are no paid exchanges performed by Tog. So, we can observe the following.
| D |
Regarding the support that SS3 provides for early classification we can say that, even though the rules we used are very simple, they are more effective than more elaborated and complex mechanisms used in the pilot task. For instance, some mechanisms to stop reading and classifying a subject included complex decision m... | Regarding document representations some research groups used simple features like standard Bag of Words [Trotzek et al., 2017, Villegas et al., 2017, Farıas-Anzaldúa et al., 2017], bigrams and trigrams [Villegas et al., 2017, Almeida et al., 2017, Farıas-Anzaldúa et al., 2017], while others used more elaborated and dom... | Most research groups [Malam et al., 2017, Trotzek et al., 2017, Sadeque et al., 2017, Villatoro-Tello et al., 2017, Villegas et al., 2017, Almeida et al., 2017] applied a simple policy in which, the same way as in [Losada & Crestani, 2016], a subject is classified as depressed when the classifier outputs a value greate... | Regarding classification models, some groups used standard classifiers777Such as Multinomial Naive Bayes(MNB), Logistic Regression (LOGREG), Support Vector Machine(SVM), Random Forest, Decision Trees, etc.[Malam et al., 2017, Trotzek et al., 2017, Sadeque et al., 2017, Villegas et al., 2017, Almeida et al., 2017, Farıa... | It is true that more elaborated methods that simultaneously learn the classification model and the policy to stop reading could have been used, such as in [Dulac-Arnold et al., 2011, Yu et al., 2017].
However, for the moment it is clear that this very simple approach is effective enough to outperform the remainder meth... | D |
Stochastic gradient descent (SGD) and its variants (Robbins and Monro, 1951; Bottou, 2010; Johnson and Zhang, 2013; Zhao et al., 2018, 2020, 2021) have been the dominating optimization methods for solving (1).
In each iteration, SGD calculates a (mini-batch) stochastic gradient and uses it to update the model parameter... | Furthermore, when we distribute the training across multiple workers, the local objective functions may differ from each other due to the heterogeneous training data distribution. In Section 5, we will demonstrate that the global momentum method outperforms its local momentum counterparts in distributed deep model trai... | Recently, parameter server (Li et al., 2014) has been one of the most popular distributed frameworks in machine learning. GMC can also be implemented on the parameter server framework.
In this paper, we adopt the parameter server framework for illustration. The theories in this paper can also be adapted for the all-red... | With the rapid growth of data, distributed SGD (DSGD) and its variant distributed MSGD (DMSGD) have garnered much attention. They distribute the stochastic gradient computation across multiple workers to expedite the model training.
These methods can be implemented on distributed frameworks like parameter server and al... | GMC can be easily implemented on the all-reduce distributed framework in which each worker sends the sparsified vector 𝒞(𝐞t+12,k)𝒞subscript𝐞𝑡12𝑘\mathcal{C}({\bf e}_{t+\frac{1}{2},k})caligraphic_C ( bold_e start_POSTSUBSCRIPT italic_t + divide start_ARG 1 end_ARG start_ARG 2 end_ARG , italic_k end_POSTSUBSCRIPT )... | C |
Olshausen et al. [43] presented an objective function that considers subjective measures of sparseness of the activation maps, however in this work we use the direct measure of compression ratio.
Previous work by [44] have used a weighted combination of the number of neurons, percentage root-mean-squared difference and... | The increased number of weights and non-zero activations make DNNs more complex, and thus more difficult to use in problems that require corresponding causality of the output with a specific set of neurons.
The majority of domains where machine learning is applied, including critical areas such as healthcare [26], requ... | A limitation of SANs is the use of varying amplitude-only kernels, which are not sufficient for more complex data and also do not fully utilize the compressibility of the data.
A possible solution would be using a grid sampler [45] on the kernel allowing it to learn more general transformations (such as scale) than sim... | It is interesting to note that in some cases SANs reconstructions, such as for the Extrema-Pool indices, performed even better than the original data.
This suggests the overwhelming presence of redundant information that resides in the raw pixels of the original data and further indicates that SANs extract the most rep... | The φ𝜑\varphiitalic_φ metric is also related to the rate-distortion theory [40], in which the maximum distortion is defined according to human perception, which however inevitably introduces a bias.
There is also relation with the field of Compressed Sensing [41] in which the sparsity of the data is exploited allowing... | B |
The essence of PBLLA is selecting an alternative UAV randomly in one iteration and improving its utility by altering power and altitude with a certain probability, which is determined by the utilities of two strategies and τ𝜏\tauitalic_τ. UAV prefers to select the power and altitude which provide higher utility. Neve... |
Since PBLLA only allows one single UAV to alter strategies in one iteration, such defect would cause computation time to grow exponentially in large-scale UAVs systems. In terms of large-scale UAVs ad-hoc networks with a number of UAVs denoted as M𝑀Mitalic_M, M2superscript𝑀2M^{2}italic_M start_POSTSUPERSCRIPT 2 end_... |
Compared with other algorithms, novel algorithm SPBLLA has more advantages in learning rate. Various algorithms have been employed in the UAV networks in search of the optimal channel selection [31][29], such as stochastic learning algorithm [30]. The most widely seen algorithm–LLA is an ideal method for NE approachin... | Fig. 15 presents the learning rate of PBLLA and SPBLLA when τ=0.01𝜏0.01\tau=0.01italic_τ = 0.01. As m𝑚mitalic_m increases the learning rate of SPBLLA decreases, which has been shown in Fig. 15. However, when m𝑚mitalic_m is small, SPBLLA’s learning rate is about 3 times that of PBLLA showing the great advantage of sy... |
The learning rate of the extant algorithm is also not desirable [13]. Recently, a new fast algorithm called binary log-linear learning algorithm (BLLA) has been proposed by [14]. However, in this algorithm, only one UAV is allowed to change strategy in one iteration based on current game state, and then another UAV ch... | A |
=ΣejBese3absentsubscript𝑒𝑗absentΣsuperscript𝐵𝑒superscript𝑠𝑒3\displaystyle=\overset{e_{j}}{\underset{}{\Sigma}}\,B^{e}\frac{s^{e}}{3}= start_OVERACCENT italic_e start_POSTSUBSCRIPT italic_j end_POSTSUBSCRIPT end_OVERACCENT start_ARG start_UNDERACCENT end_UNDERACCENT start_ARG roman_Σ end_ARG end_ARG italic_B st... | U¯r′superscriptsubscript¯𝑈𝑟′\displaystyle\overline{U}_{r}^{\prime}over¯ start_ARG italic_U end_ARG start_POSTSUBSCRIPT italic_r end_POSTSUBSCRIPT start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT
=Dr¯¯∗U¯absent¯¯𝐷𝑟¯𝑈\displaystyle=\overline{\overline{Dr}}*\overline{U}= over¯ start_ARG over¯ start_ARG italic_D italic_r e... | U^r′superscriptsubscript^𝑈𝑟′\displaystyle\widehat{U}_{r}^{\prime}over^ start_ARG italic_U end_ARG start_POSTSUBSCRIPT italic_r end_POSTSUBSCRIPT start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT
=Dr^¯∗U¯absent¯^𝐷𝑟¯𝑈\displaystyle=\overline{\widehat{Dr}}*\overline{U}= over¯ start_ARG over^ start_ARG italic_D italic_r end... | =S¯¯−1∗(M^¯T∗S^^∗Dr^¯)absentsuperscript¯¯𝑆1superscript¯^𝑀𝑇^^𝑆¯^𝐷𝑟\displaystyle=\overline{\overline{S}}^{-1}*\left(\overline{\widehat{M}}^{T}*%
\widehat{\widehat{S}}*\overline{\widehat{Dr}}\right)= over¯ start_ARG over¯ start_ARG italic_S end_ARG end_ARG start_POSTSUPERSCRIPT - 1 end_POSTSUPERSCRIPT ∗ ( over¯ sta... | U¯r′superscriptsubscript¯𝑈𝑟′\displaystyle\overline{U}_{r}^{\prime}over¯ start_ARG italic_U end_ARG start_POSTSUBSCRIPT italic_r end_POSTSUBSCRIPT start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT
=(S¯¯−1∗(M^¯T∗S^^∗Dr^¯))∗U¯absentsuperscript¯¯𝑆1superscript¯^𝑀𝑇^^𝑆¯^𝐷𝑟¯𝑈\displaystyle=\left(\overline{\overline{S}}^{-1}... | D |
When using the framework, one can further require reflexivity on the comparability functions, i.e. f(xA,xA)=1A𝑓subscript𝑥𝐴subscript𝑥𝐴subscript1𝐴f(x_{A},x_{A})=1_{A}italic_f ( italic_x start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT , italic_x start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT ) = 1 start_POSTSUBSCRIP... | Intuitively, if an abstract value xAsubscript𝑥𝐴x_{A}italic_x start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT of ℒAsubscriptℒ𝐴\mathcal{L}_{A}caligraphic_L start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT is interpreted as 1111 (i.e., equality)
by hAsubscriptℎ𝐴h_{A}italic_h start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT... | When using the framework, one can further require reflexivity on the comparability functions, i.e. f(xA,xA)=1A𝑓subscript𝑥𝐴subscript𝑥𝐴subscript1𝐴f(x_{A},x_{A})=1_{A}italic_f ( italic_x start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT , italic_x start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT ) = 1 start_POSTSUBSCRIP... | fA(u,v)=fB(u,v)={1if u=v≠nullaif u≠null,v≠null and u≠vbif u=v=null0otherwise.subscript𝑓𝐴𝑢𝑣subscript𝑓𝐵𝑢𝑣cases1if 𝑢𝑣null𝑎formulae-sequenceif 𝑢null𝑣null and 𝑢𝑣𝑏if 𝑢𝑣null0otherwise.f_{A}(u,v)=f_{B}(u,v)=\begin{cases}1&\text{if }u=v\neq\texttt{null}\\
a&\text{if }u\neq\texttt{null},v\neq\texttt{null}... | Indeed, in practice the meaning of the null value in the data should be explained by domain experts, along with recommendations on how to deal with it.
Moreover, since the null value indicates a missing value, relaxing reflexivity of comparability functions on null allows to consider absent values as possibly | D |
To that end, we ran Dropout-DQN and DQN on one of the classic control environments to express the effect of Dropout on Variance and the learned policies quality. For the Overestimation phenomena, we ran Dropout-DQN and DQN on a Gridworld environment to express the effect of Dropout because in such environment the optim... |
Reinforcement Learning (RL) is a learning paradigm that solves the problem of learning through interaction with environments, this is a totally different approach from the other learning paradigms that have been studied in the field of Machine Learning namely the supervised learning and the unsupervised learning. Rein... |
To evaluate the Dropout-DQN, we employ the standard reinforcement learning (RL) methodology, where the performance of the agent is assessed at the conclusion of the training epochs. Thus we ran ten consecutive learning trails and averaged them. We have evaluated Dropout-DQN algorithm on CARTPOLE problem from the Class... |
The sources of DQN variance are Approximation Gradient Error(AGE)[23] and Target Approximation Error(TAE)[24]. In Approximation Gradient Error, the error in gradient direction estimation of the cost function leads to inaccurate and extremely different predictions on the learning trajectory through different episodes b... | To that end, we ran Dropout-DQN and DQN on one of the classic control environments to express the effect of Dropout on Variance and the learned policies quality. For the Overestimation phenomena, we ran Dropout-DQN and DQN on a Gridworld environment to express the effect of Dropout because in such environment the optim... | B |
As one of the first high impact CNN-based segmentation models, Long et al. (2015) proposed fully convolutional networks for pixel-wise labeling. They proposed up-sampling (deconvolving) the output activation maps from which the pixel-wise output can be calculated. The overall architecture of the network is visualized ... |
As one of the first high impact CNN-based segmentation models, Long et al. (2015) proposed fully convolutional networks for pixel-wise labeling. They proposed up-sampling (deconvolving) the output activation maps from which the pixel-wise output can be calculated. The overall architecture of the network is visualized ... |
Several modified versions (e.g. deeper/shallower, adding extra attention blocks) of encoder-decoder networks have been applied to semantic segmentation (Amirul Islam et al., 2017; Fu et al., 2019b; Lin et al., 2017a; Peng et al., 2017; Pohlen et al., 2017; Wojna et al., 2017; Zhang et al., 2018d). Recently in 2018, De... |
In order to preserve the contextual spatial information within an image as the filtered input data progresses deeper into the network, Long et al. (2015) proposed to fuse the output with shallower layers’ output. The fusion step is visualized in Figure 4. | Vorontsov et al. (2019), using a dataset defined in Cohen et al. (2018), proposed an image-to-image based framework to transform an input image with object of interest (presence domain) like a tumor to an image without the tumor (absence domain) i.e. translate diseased image to healthy; next, their model learns to add ... | C |
Interestingly, the Dense architecture achieves the best performance on MUTAG, indicating that in this case, the connectivity of the graps does not carry useful information for the classification task.
The performance of the Flat baseline indicates that in Enzymes and COLLAB pooling operations are not necessary to impro... | Contrarily to graph classification, DiffPool and TopK𝐾Kitalic_K fail to solve this task and achieve an accuracy comparable to random guessing.
On the contrary, the topological pooling methods obtain an accuracy close to a classical CNN, with NDP significantly outperforming the other two techniques. |
When compared to other methods for graph pooling, NDP performs significantly better than other techniques that pre-compute the topology of the coarsened graphs, while it achieves a comparable performance with respect to state-of-the-art feature-based pooling methods. | In Fig. 7, we report the training time for the five different pooling methods.
As expected, GNNs configured with GRACLUS, NMF, and NDP are much faster to train compared to those based on DiffPool and TopK𝐾Kitalic_K, with NDP being slightly faster than the other two topological methods. | Figure 9: Example of coarsening on one graph from the Proteins dataset. In (a), the original adjacency matrix of the graph. In (b), (c), and (d) the edges of the Laplacians at coarsening level 0, 1, and 2, as obtained by the 3 different pooling methods GRACLUS, NMF, and the proposed NDP.
| C |
where wD∈ℝnTsuperscript𝑤𝐷superscriptℝsubscript𝑛𝑇w^{D}\in\mathbb{R}^{n_{T}}italic_w start_POSTSUPERSCRIPT italic_D end_POSTSUPERSCRIPT ∈ blackboard_R start_POSTSUPERSCRIPT italic_n start_POSTSUBSCRIPT italic_T end_POSTSUBSCRIPT end_POSTSUPERSCRIPT. This optimization finds a weighting of the number of decision trees... |
The proposed method generates data from a random forest and trains a neural network that imitates the random forest. The goal is that the neural network approximates the same function as the random forest. This also implies that the network reaches the same accuracy if successful. | Our proposed approach, called Neural Random Forest Imitation (NRFI), implicitly transforms random forests into neural networks.
The main concept includes (1) generating training data from decision trees and random forests, (2) adding strategies for reducing conflicts and increasing the variety of the generated examples... | Finally, a neural network that imitates the random forest is trained. The network learns the decision boundaries from the generated data and approximates the same function as the random forest.
The network architecture is based on a fully connected network with one or multiple hidden layers. | NRFI with and without the original data is shown for different network architectures. The smallest architecture has 2222 neurons in both hidden layers and the largest 128128128128. For NRFI (gen-ori), we can see that a network with 16161616 neurons in both hidden layers (NN-16-16) is already sufficient to learn the dec... | C |
Coupled with powerful function approximators such as neural networks, policy optimization plays a key role in the tremendous empirical successes of deep reinforcement learning (Silver et al., 2016, 2017; Duan et al., 2016; OpenAI, 2019; Wang et al., 2018). In sharp contrast, the theoretical understandings of policy opt... |
A line of recent work (Fazel et al., 2018; Yang et al., 2019a; Abbasi-Yadkori et al., 2019a, b; Bhandari and Russo, 2019; Liu et al., 2019; Agarwal et al., 2019; Wang et al., 2019) answers the computational question affirmatively by proving that a wide variety of policy optimization algorithms, such as policy gradient... | Our work is based on the aforementioned line of recent work (Fazel et al., 2018; Yang et al., 2019a; Abbasi-Yadkori et al., 2019a, b; Bhandari and Russo, 2019; Liu et al., 2019; Agarwal et al., 2019; Wang et al., 2019) on the computational efficiency of policy optimization, which covers PG, NPG, TRPO, PPO, and AC. In p... | for any function f:𝒮→ℝ:𝑓→𝒮ℝf:{\mathcal{S}}\rightarrow\mathbb{R}italic_f : caligraphic_S → blackboard_R. By allowing the reward function to be adversarially chosen in each episode, our setting generalizes the stationary setting commonly adopted by the existing work on value-based reinforcement learning (Jaksch et al.... |
Broadly speaking, our work is related to a vast body of work on value-based reinforcement learning in tabular (Jaksch et al., 2010; Osband et al., 2014; Osband and Van Roy, 2016; Azar et al., 2017; Dann et al., 2017; Strehl et al., 2006; Jin et al., 2018) and linear settings (Yang and Wang, 2019b, a; Jin et al., 2019;... | A |
In experiments, we demonstrated on two benchmark data sets the difficulty of finding a good trade-off among prediction quality, representational efficiency and computational efficiency.
Considering three embedded hardware platforms, we showed that massive parallelism is required for inference efficiency and that quanti... | We furthermore point out that hardware properties and the corresponding computational efficiency form a large fraction of resource efficiency.
This highlights the need to consider particular hardware targets when searching for resource-efficient machine learning models. | In experiments, we demonstrated on two benchmark data sets the difficulty of finding a good trade-off among prediction quality, representational efficiency and computational efficiency.
Considering three embedded hardware platforms, we showed that massive parallelism is required for inference efficiency and that quanti... | The computational cost of performing inference should match the (usually limited) resources in deployed systems and exploit the available hardware optimally in terms of time and energy.
Computational efficiency, in particular, also includes mapping the representational efficiency to available hardware structures. | In this regard, resource-efficient neural networks for embedded systems are concerned with the trade-off between prediction quality and resource efficiency (i.e., representational efficiency and computational efficiency). This is highlighted in Figure 1.
Note that this requires observing overall constraints such as pre... | A |
In Section 7, we prove a number of results concerning the homotopy types of Vietoris-Rips filtrations of spheres and complex projective spaces. Also, we fully compute the homotopy types of Vietoris-Rips filtration of spheres with ℓ∞superscriptℓ\ell^{\infty}roman_ℓ start_POSTSUPERSCRIPT ∞ end_POSTSUPERSCRIPT-norm. | In Section 8, we reprove Rips and Gromov’s result about the contractibility of the Vietoris-Rips complex of hyperbolic geodesic metric spaces, by using our method consisting of isometric embeddings into injective metric spaces. As a result, we will be able to bound the length of intervals in Vietoris-Rips persistence b... | The simplicial complex nowadays referred to as the Vietoris-Rips complex was originally introduced by Leopold Vietoris in the early 1900s in order to build a homology theory for metric spaces [79]. Later, Eliyahu Rips and Mikhail Gromov [47] both utilized the Vietoris-Rips complex in their study of hyperbolic groups.
| Of central interest in topological data analysis has been the question of providing a complete characterization of the Vietoris-Rips persistence barcodes of spheres of different dimensions. Despite the existence of a complete answer to the question for the case of 𝕊1superscript𝕊1\mathbb{S}^{1}blackboard_S start_POSTS... |
In Section 7, we prove a number of results concerning the homotopy types of Vietoris-Rips filtrations of spheres and complex projective spaces. Also, we fully compute the homotopy types of Vietoris-Rips filtration of spheres with ℓ∞superscriptℓ\ell^{\infty}roman_ℓ start_POSTSUPERSCRIPT ∞ end_POSTSUPERSCRIPT-norm. | A |
One way to obtain an indication of a projection’s quality is to compute a single scalar value, equivalent to a final score. Examples are Normalized Stress [7], Trustworthiness and Continuity [24], and Distance Consistency (DSC) [25]. More recently, ClustMe [26] was proposed as a perception-based measure that ranks scat... | We present a Neighborhood Preservation plot (Figure 1(g)) that shows an overview of the preservation of neighborhoods of different sizes (k𝑘kitalic_k) in both the entire projection and the current selection, based on the Jaccard distance between sets:
| we present t-viSNE, a tool designed to support the interactive exploration of t-SNE projections (an extension to our previous poster abstract [17]). In contrast to other, more general approaches, t-viSNE was designed with the specific problems related to the investigation of t-SNE projections in mind, bringing to light... | The difference line plot (d), on the other hand, builds on the standard plot by highlighting the differences between the selection and the global average, shown as positive and negative values around the 0 value of the y-axis.
It provides a clearer overall picture of the difference in preservation among all the shown s... | As an example, the set difference from Martins et al. [33] uses the Jaccard set-distance between the two sets of neighbors of a point in low- and high-dimensional space in order to compute a measure of Neighborhood Preservation. We have chosen to adopt it in our work, in contrast to others, because of its intuitive int... | D |
Similarity in metaheuristics: A gentle step towards a comparison methodology - 2022 [27]: This paper uses a pool template as a framework for decomposing and analyzing metaheuristics, inspired by another previous work. This template works as a framework for decomposing and analyzing metaheuristics based on these concept... |
50 years of metaheuristics - 2024 [40]: This overview traces the last 50 years of the field, starting from the roots of the area to the latest proposals to hybridize metaheuristics with machine learning. The revision encompasses constructive (GRASP and ACO), local search (iterated local search, Tabu search, variable n... | Good practices for designing metaheuristics: It gathers several works that are guidelines for good practices related to research orientation to measure novelty [26], to measure similarity in metaheuristics [27], Metaheuristics “In the Large” (to support the development, analysis, and comparison of new approaches) [28],... | Metaheuristics “In the Large” - 2022 [28]: The objective of this work is to provide a useful tool for researchers. To address the lack of novelty, the authors propose a new infrastructure to support the development, analysis, and comparison of new approaches. This framework is based on (1) the use of algorithm template... |
The constant evolution of the field leads to a significant issue: the lack of novelty in metaheuristics. However, researchers recognize the need to address this problem and have proposed methods to evaluate the novelty of new algorithms. This section shows different studies and guidelines to measure novelty, to design... | C |
Figure 1: Framework of AdaGAE. k0subscript𝑘0k_{0}italic_k start_POSTSUBSCRIPT 0 end_POSTSUBSCRIPT is the initial sparsity. First, we construct a sparse graph via the generative model defined in Eq. (7). The learned graph is employed to apply the GAE designed for the weighted graphs. After training the GAE, we update ... | As well as the well-known k𝑘kitalic_k-means [1, 2, 3], graph-based clustering [4, 5, 6] is also a representative kind of clustering method.
Graph-based clustering methods can capture manifold information so that they are available for the non-Euclidean type data, which is not provided by k𝑘kitalic_k-means. Therefore,... | (1) Via extending the generative graph models into general type data, GAE is naturally employed as the basic representation learning model and weighted graphs can be further applied to GAE as well. The connectivity distributions given by the generative perspective also inspires us to devise a novel architecture for dec... |
In recent years, GCNs have been studied a lot to extend neural networks to graph type data. How to design a graph convolution operator is a key issue and has attracted a mass of attention. Most of them can be classified into 2 categories, spectral methods [24] and spatial methods[25]. | However, the existing methods are limited to graph type data while no graph is provided for general data clustering. Since a large proportion of clustering methods are based on the graph, it is reasonable to consider how to employ GCN to promote the performance of graph-based clustering methods.
In this paper, we propo... | D |
We also want to understand the types of networks that we could test via domains-wide scans. To derive the business types we use the PeeringDB. We classify the ASes according to the following business types: content, enterprise, Network Service Provider (NSP), Cable/DSL/ISP, non-profit, educational/research, route serve... | There is a strong correlation between the AS size and the enforcement of spoofing, see Figure 13. Essentially, the larger the AS, the higher the probability that our tools identify that it does not filter spoofed packets. The reason can be directly related to our methodologies and the design of our study: the larger th... |
SMap (The Spoofing Mapper). In this work we present the first Internet-wide scanner for networks that filter spoofed inbound packets, we call the Spoofing Mapper (SMap). We apply SMap for scanning ingress-filtering in more than 90% of the Autonomous Systems (ASes) in the Internet. The measurements with SMap show that ... |
Domain-scan and IPv4-scan both show that the number of spoofable ASes grows with the overall number of the ASes in the Internet, see Figure 1. Furthermore, there is a correlation between fraction of scanned domains and ASes. Essentially the more domains are scanned, the more ASes are covered, and more spoofable ASes a... | Identifying servers with global IPID counters. We send packets from two hosts (with different IP addresses) to a server on a tested network. We implemented probing over TCP SYN, ping and using requests/responses to Name servers and we apply the suitable test depending on the server that we identify on the tested networ... | A |
Machine learning applications frequently deal with data-generating processes that change over time. Applications in such nonstationary environments include power use forecasting, recommendation systems, and environmental sensors [9]. Semisupervised learning, which has received a lot of attention in the sensor communit... | Biology frequently deals with drift [16]. For instance olfactory systems are constantly adapting, predominantly through feedback mechanisms. This section details some such models from computer science and neuroscience [17]. One example is the KIII model, a dynamic network resembling the olfactory bulb and feedforward a... |
The purpose of this study was to demonstrate that explicit representation of context can allow a classification system to adapt to sensor drift. Several gas classifier models were placed in a setting with progressive sensor drift and were evaluated on samples from future contexts. This task reflects the practical goal... | While natural systems cope with changing environments and embodiments well, they form a serious challenge for artificial systems. For instance, to stay reliable over time, gas sensing systems must be continuously recalibrated to stay accurate in a changing physical environment. Drawing motivation from nature, this pape... |
One prominent feature of the mammalian olfactory system is feedback connections to the olfactory bulb from higher-level processing regions. Activity in the olfactory bulb is heavily influenced by behavioral and value-based information [19], and in fact, the bulb receives more neural projections from higher-level regio... | A |
The goal would be to obtain an algorithm with running time 2O(f(δ)n)superscript2𝑂𝑓𝛿𝑛2^{O(f(\delta)\sqrt{n})}2 start_POSTSUPERSCRIPT italic_O ( italic_f ( italic_δ ) square-root start_ARG italic_n end_ARG ) end_POSTSUPERSCRIPT, where f(n)=O(n1/6)𝑓𝑛𝑂superscript𝑛16f(n)=O(n^{1/6})italic_f ( italic_n ) = italic... | It would be interesting to see whether a direct proof can be given for this fundamental result.
We note that the proof of Theorem 2.1 can easily be adapted to point sets of which the x𝑥xitalic_x-coordinates of the points need not be integer, as long as the difference between x𝑥xitalic_x-coordinates of any two consecu... | First of all, the ΔisubscriptΔ𝑖\Delta_{i}roman_Δ start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT are now independent.
Second, as we will prove next, the expected running time of an algorithm on a uniformly distributed point set can be bounded by the expected running time of that algorithm on a point set generated this ... | We believe that our algorithm can serve as the basis of an algorithm solving such a problem, under the assumption that the point sets are dense enough to ensure that the solution will generally follow these curves / segments. Making this precise, and investigating how the running time depends on the number of line segm... | In the second step, we therefore describe a method to generate the random point set in a different way, and we show how to relate the expected running times in these two settings.
In the third step, we will explain which changes are made to the algorithm. | C |
The problem of presenting (finitely generated) free groups and semigroups in a self-similar way has a long history [15]. A self-similar presentation in this context is typically a faithful action on an infinite regular tree (with finite degree) such that, for any element and any node in the tree, the action of the elem... |
There are quite a few results on free (and related) products of self-similar or automaton groups (again see [15] for an overview) but many of them present the product as a subgroup of an automaton/self-similar group and, thus, loose the self-similarity property. An exception here is a line of research based on the Bel... | 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). As an example, we prove in the following theorem that it is possible to adjoin a free generator to every self-similar semigroup without losing ... |
There is a quite interesting evolution of constructions to present free groups in a self-similar way or even as automaton groups (see [15] for an overview). This culminated in constructions to present free groups of arbitrary rank as automaton groups where the number of states coincides with the rank [18, 17]. While t... | from one to the other, then their free product S⋆T⋆𝑆𝑇S\star Titalic_S ⋆ italic_T is an automaton semigroup (8). This is again a strict generalization of [19, Theorem 3.0.1] (even if we only consider complete automata).
Third, we show this result in the more general setting of self-similar semigroups111Note that the c... | C |
SCR divides the region proposals into influential and non-influential regions and penalizes the model if: 1) 𝒮(agt)𝒮subscript𝑎𝑔𝑡\mathcal{S}(a_{gt})caligraphic_S ( italic_a start_POSTSUBSCRIPT italic_g italic_t end_POSTSUBSCRIPT ) of a non-influential region is higher than an influential region, and 2) the regio... | Here, we study these methods. We find that their improved accuracy does not actually emerge from proper visual grounding, but from regularization effects, where the model forgets the linguistic priors in the train set, thereby performing better on the test set. To support these claims, we first show that it is possible... | We probe the reasons behind the performance improvements of HINT and SCR. We first analyze if the results improve even when the visual cues are irrelevant (Sec. 4.2) or random (Sec. 4.3) and examine if their differences are statistically significant (Sec. 4.4). Then, we analyze the regularization effects by evaluating ... | We test our regularization method on random subsets of varying sizes. Fig. A6 shows the results when we apply our loss to 1−100%1percent1001-100\%1 - 100 % of the training instances. Clearly, the ability to regularize the model does not vary much with respect to the size of the train subset, with the best performance o... | As observed by Selvaraju et al. (2019) and as shown in Fig. 2, we observe small improvements on VQAv2 when the models are fine-tuned on the entire train set. However, if we were to compare against the improvements in VQA-CPv2 in a fair manner, i.e., only use the instances with visual cues while fine-tuning, then, the p... | B |
To satisfy the need for a much larger corpus of privacy policies, we introduce the PrivaSeer Corpus of 1,005,380 English language website privacy policies. The number of unique websites represented in PrivaSeer is about ten times larger than the next largest public collection of web privacy policies Amos et al. (2020)... |
For the data practice classification task, we leveraged the OPP-115 Corpus introduced by Wilson et al. (2016). The OPP-115 Corpus contains manual annotations of 23K fine-grained data practices on 115 privacy policies annotated by legal experts. To the best of our knowledge, this is the most detailed and widely used da... |
Prior collections of privacy policy corpora have led to progress in privacy research. Wilson et al. (2016) released the OPP-115 Corpus, a dataset of 115 privacy policies with manual annotations of 23k fine-grained data practices, and they created a baseline for classifying privacy policy text into one of ten categorie... | Other corpora similar to OPP-115 Corpus have enabled research on privacy practices. The PrivacyQA corpus contains 1,750 questions and expert-annotated answers for the privacy question answering task (Ravichander et al., 2019). Similarly, Lebanoff and Liu (2018) constructed the first corpus of human-annotated vague word... |
Natural language processing (NLP) provides an opportunity to automate the extraction of salient details from privacy policies, thereby reducing human effort and enabling the creation of tools for internet users to understand and control their online privacy. Existing research has achieved some success using expert ann... | B |
Figure 1: Knowledge generation model for ensemble learning with VA derived from the model by Sacha et al. [44]. On the left, it illustrates how a VA system can enable the exploration of the data and the models with the use of visualization. On the right, a number of design goals assist the human in the exploration, ve... | In a bucket of models, the best model for a specific problem is automatically chosen from a set of available options. This strategy is conceptually different to the ideas of bagging, boosting, and stacking, but still related to ensemble learning.
Chen et al. [6] utilize a bucket of latent Dirichlet allocation (LDA) mod... | The rest of this paper is organized as follows. In the next section, we discuss the literature related to visualization of ensemble learning.
Afterwards, we describe the knowledge generation model for ensemble learning with VA, design goals, and analytical tasks for attaching VA to ensemble learning. | Visualization systems have been developed for the exploration of diverse aspects of bagging, boosting, and further strategies such as “bucket of models”.
Stacking, however, has so far not received comparable attention by the InfoVis/VA communities: actually, we have not found any literature describing the construction ... |
Figure 1: Knowledge generation model for ensemble learning with VA derived from the model by Sacha et al. [44]. On the left, it illustrates how a VA system can enable the exploration of the data and the models with the use of visualization. On the right, a number of design goals assist the human in the exploration, ve... | C |
We thus have 3333 cases, depending on the value of the tuple
(p(v,[010]),p(v,[323]),p(v,[313]),p(v,[003]))𝑝𝑣delimited-[]010𝑝𝑣delimited-[]323𝑝𝑣delimited-[]313𝑝𝑣delimited-[]003(p(v,[010]),p(v,[323]),p(v,[313]),p(v,[003]))( italic_p ( italic_v , [ 010 ] ) , italic_p ( italic_v , [ 323 ] ) , italic_p ( italic_v... | Then, by using the adjacency of (v,[013])𝑣delimited-[]013(v,[013])( italic_v , [ 013 ] ) with each of
(v,[010])𝑣delimited-[]010(v,[010])( italic_v , [ 010 ] ), (v,[323])𝑣delimited-[]323(v,[323])( italic_v , [ 323 ] ), and (v,[112])𝑣delimited-[]112(v,[112])( italic_v , [ 112 ] ), we can confirm that | By using the pairwise adjacency of (v,[112])𝑣delimited-[]112(v,[112])( italic_v , [ 112 ] ), (v,[003])𝑣delimited-[]003(v,[003])( italic_v , [ 003 ] ), and
(v,[113])𝑣delimited-[]113(v,[113])( italic_v , [ 113 ] ), we can confirm that in the 3333 cases, these | {0¯,1¯,2¯,3¯,[013],[010],[323],[313],[112],[003],[113]}.¯0¯1¯2¯3delimited-[]013delimited-[]010delimited-[]323delimited-[]313delimited-[]112delimited-[]003delimited-[]113\{\overline{0},\overline{1},\overline{2},\overline{3},[013],[010],[323],[313],%
[112],[003],[113]\}.{ over¯ start_ARG 0 end_ARG , over¯ start_ARG 1 end... | p(v,[013])=p(v,[313])=p(v,[113])=1𝑝𝑣delimited-[]013𝑝𝑣delimited-[]313𝑝𝑣delimited-[]1131p(v,[013])=p(v,[313])=p(v,[113])=1italic_p ( italic_v , [ 013 ] ) = italic_p ( italic_v , [ 313 ] ) = italic_p ( italic_v , [ 113 ] ) = 1.
Similarly, when f=[112]𝑓delimited-[]112f=[112]italic_f = [ 112 ], | B |
When applying MAML to NLP, several factors can influence the training strategy and performance of the model. Firstly, the data quantity within the datasets used as ”tasks” varies across different applications, which can impact the effectiveness of MAML [Serban et al., 2015, Song et al., 2020]. Secondly, while vanilla ... | In this paper, we take an empirical approach to systematically investigating these impacting factors and finding when MAML works the best. We conduct extensive experiments over 4 datasets. We first study the effects of data quantity and distribution on the training strategy:
RQ1. Since the parameter initialization lear... |
To answer RQ1, we compare the changing trend of the general language model and the task-specific adaptation ability during the training of MAML to find whether there is a trade-off problem. (Figure 1) We select the trained parameter initialization at different MAML training epochs and evaluate them directly on the met... | The finding suggests that parameter initialization at the late training stage has strong general language generation ability, but performs comparative poorly in task-specific adaptation.
Although in the early training stage, the performance improves benefiting from the pre-trained general language model, if the languag... |
When applying MAML to NLP, several factors can influence the training strategy and performance of the model. Firstly, the data quantity within the datasets used as ”tasks” varies across different applications, which can impact the effectiveness of MAML [Serban et al., 2015, Song et al., 2020]. Secondly, while vanilla ... | A |
In this paper, we consider a dynamic mission-driven UAV network with UAV-to-UAV mmWave communications, wherein multiple transmitting UAVs (t-UAVs) simultaneously transmit to a receiving UAV (r-UAV). In such a scenario, we focus on inter-UAV communications in UAV networks, and the UAV-to-ground communications are not in... |
The first study on the beam tracking framework for CA-enabled UAV mmWave networks. We propose an overall beam tracking framework to exemplify the idea of the DRE-covered CCA integrated with UAVs, and reveal that CA can offer full-spatial coverage and facilitate beam tracking, thus enabling high-throughput inter-UAV da... |
The specialized codebook design of the DRE-covered CCA for multi-UAV mobile mmWave communications. Under the guidance of the proposed framework, a novel hierarchical codebook is designed to encompass both the subarray patterns and beam patterns. The newly proposed CA codebook can fully exploit the potentials of the DR... |
When considering UAV communications with UPA or ULA, a UAV is typically modeled as a point in space without considering its size and shape. Actually, the size and shape can be utilized to support more powerful and effective antenna array. Inspired by this basic consideration, the conformal array (CA) [16] is introduce... | For both static and mobile mmWave networks, codebook design is of vital importance to empower the feasible beam tracking and drive the mmWave antenna array for reliable communications [22, 23]. Recently, ULA/UPA-oriented codebook designs have been proposed for mmWave networks, which include the codebook-based beam trac... | A |
Thus,
a¯|b¯conditional¯𝑎¯𝑏\bar{a}|\bar{b}over¯ start_ARG italic_a end_ARG | over¯ start_ARG italic_b end_ARG-regular digraphs with size M¯¯𝑀\bar{M}over¯ start_ARG italic_M end_ARG can be characterized as a¯|b¯conditional¯𝑎¯𝑏\bar{a}|\bar{b}over¯ start_ARG italic_a end_ARG | over¯ start_ARG italic_b end_ARG-biregula... | We start in this section by giving proofs only for the 1111-color case, without the completeness requirement. While this case does not directly correspond to any formula used in the proof of Theorem 3.7 (since matrices (4) have 2 rows even when there are no binary predicates), this case gives the flavor of the argument... | The case of fixed degree and multiple colors is done via an induction, using merging and then swapping to eliminate parallel edges.
The case of unfixed degree is handled using a case analysis depending on whether sizes are “big enough”, but the approach is different from | This will be bootstrapped to the multi-color case in later sections. 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 ri... | To conclude this section, we stress that although the 1111-color case contains many of the key ideas, the multi-color case requires a finer
analysis to deal with the “big enough” case, and also may benefit from a reduction that allows one to restrict | D |
In this paper, we study temporal-difference (TD) (Sutton, 1988) and Q-learning (Watkins and Dayan, 1992), two of the most prominent algorithms in deep reinforcement learning, which are further connected to policy gradient (Williams, 1992) through its equivalence to soft Q-learning (O’Donoghue et al., 2016; Schulman et... |
Contribution. Going beyond the NTK regime, we prove that, when the value function approximator is an overparameterized two-layer neural network, TD and Q-learning globally minimize the mean-squared projected Bellman error (MSPBE) at a sublinear rate. Moreover, in contrast to the NTK regime, the induced feature represe... | Szepesvári, 2018; Dalal et al., 2018; Srikant and Ying, 2019) settings. See Dann et al. (2014) for a detailed survey. Also, when the value function approximator is linear, Melo et al. (2008); Zou et al. (2019); Chen et al. (2019b) study the convergence of Q-learning. When the value function approximator is nonlinear, T... |
corresponding to θ(m)(k)=(θ1(k),…,θm(k))∈ℝD×msuperscript𝜃𝑚𝑘subscript𝜃1𝑘…subscript𝜃𝑚𝑘superscriptℝ𝐷𝑚\theta^{(m)}(k)=(\theta_{1}(k),\ldots,\theta_{m}(k))\in\mathbb{R}^{D\times m}italic_θ start_POSTSUPERSCRIPT ( italic_m ) end_POSTSUPERSCRIPT ( italic_k ) = ( italic_θ start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT (... | To address such an issue of divergence, nonlinear gradient TD (Bhatnagar et al., 2009) explicitly linearizes the value function approximator locally at each iteration, that is, using its gradient with respect to the parameter as an evolving feature representation. Although nonlinear gradient TD converges, it is unclear... | D |
Table 4 shows that, even though this is counter-intuitive, element-wise addition (with fewer parameters) empirically results in slightly higher BLEU than the concatenation operation. Furthermore, even though using 2 depth-wise LSTM sub-layers connecting cross- and masked self-attention sub-layers leads to the highest ... | Directly replacing residual connections with LSTM units will introduce a large amount of additional parameters and computation. Given that the task of computing the LSTM hidden state is similar to the feed-forward sub-layer in the original Transformer layers, we propose to replace the feed-forward sub-layer with the ne... |
Table 5 shows that: 1) Sharing parameters for the computation (Equation 6) of the depth-wise LSTM hidden state significantly hampers performance, which is consistent with our conjecture. 2) Sharing parameters for the computation of gates (Equations 2, 3, 4) leads to slightly higher BLEU with fewer parameters introduce... |
In our approach (“with depth-wise LSTM”), we used the 2-layer neural network for the computation of the LSTM hidden state (Equation 6) and shared LSTM parameters across stacked encoder layers and different shared parameters across decoder layers for computing the LSTM gates (Equations 2, 3, 4). Details are provided in... | As the number of Transformer layers is pre-specified, the parameters of the depth-wise LSTM can either be shared across layers or be independent. Table 3 documents the importance of the capacity of the module for the hidden state computation, and sharing the module is likely to hurt its capacity. We additionally study ... | D |
the corresponding Alexandroff topologies:
X≜⟨X,τ→,𝖥𝖮[σ]⟩≜𝑋𝑋subscriptτ→𝖥𝖮delimited-[]σX\triangleq\left\langle X,\uptau_{\to},\mathsf{FO}[\upsigma]\right\rangleitalic_X ≜ ⟨ italic_X , roman_τ start_POSTSUBSCRIPT → end_POSTSUBSCRIPT , sansserif_FO [ roman_σ ] ⟩ and for n∈ℕ𝑛ℕn\in\mathbb{N}italic_n ∈ blackboard_N, l... | For A∈Fin(σ)𝐴FinσA\in\operatorname{Fin}(\upsigma)italic_A ∈ roman_Fin ( roman_σ ) and n≥1𝑛1n\geq 1italic_n ≥ 1, there exists a structure
Coren(A)superscriptCore𝑛𝐴\operatorname{Core}^{n}(A)roman_Core start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT ( italic_A ) of tree-depth at most n𝑛nitalic_n such that | A→nCoren(A)subscript→𝑛𝐴superscriptCore𝑛𝐴A\to_{n}\operatorname{Core}^{n}(A)italic_A → start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT roman_Core start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT ( italic_A ), Coren(A)→nAsubscript→𝑛superscriptCore𝑛𝐴𝐴\operatorname{Core}^{n}(A)\to_{n}Aroman_Core start_POSTSUPERSC... | For all A∈Fin(σ)𝐴FinσA\in\operatorname{Fin}(\upsigma)italic_A ∈ roman_Fin ( roman_σ ), let ψA𝖤𝖥𝖮superscriptsubscript𝜓𝐴𝖤𝖥𝖮\psi_{A}^{\mathsf{EFO}}italic_ψ start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT start_POSTSUPERSCRIPT sansserif_EFO end_POSTSUPERSCRIPT be the
diagram sentence such that ⟦ψA𝖤𝖥𝖮⟧Struct(σ)... | all n≥1𝑛1n\geq 1italic_n ≥ 1, if A∈X𝐴𝑋A\in Xitalic_A ∈ italic_X then Coren(A)∈XsuperscriptCore𝑛𝐴𝑋\operatorname{Core}^{n}(A)\in Xroman_Core start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT ( italic_A ) ∈ italic_X since X𝑋Xitalic_X is
downwards closed. | A |
Qualitative Comparison: To qualitatively show the performance of different learning representations, we visualize the 3D distortion distribution maps (3D DDM) derived from the ground truth and these two schemes in Fig. 8, in which each pixel value of the distortion distribution map represents the distortion level. Sinc... |
Figure 11: Qualitative evaluations of the rectified distorted images on indoor (left) and outdoor (right) scenes. For each evaluation, we show the distorted image, ground truth, and corrected results of the compared methods: Alemán-Flores [23], Santana-Cedrés [24], Rong [8], Li [11], and Liao [12], and rectified resul... | We visually compare the corrected results from our approach with state-of-the-art methods using our synthetic test set and the real distorted images. To show the comprehensive rectification performance under different scenes, we split the test set into four types of scenes: indoor, outdoor, people, and challenging scen... | Figure 12: Qualitative evaluations of the rectified distorted images on people (left) and challenging (right) scenes. For each evaluation, we show the distorted image, ground truth, and corrected results of the compared methods: Alemán-Flores [23], Santana-Cedrés [24], Rong [8], Li [11], and Liao [12], and rectified re... | Figure 13: Qualitative evaluations of the rectified distorted images on real-world scenes. For each evaluation, we show the distorted image and corrected results of the compared methods: Alemán-Flores [23], Santana-Cedrés [24], Rong [8], Li [11], and Liao [12], and rectified results of our proposed approach, from left ... | A |
Apart from these empirical findings, there have been some theoretical
studies on large-batch training. For example, the convergence analyses of LARS have been reported in [34]. The work in [37] analyzed the inconsistency bias in decentralized momentum SGD and proposed DecentLaM for decentralized large-batch training. | We don’t use training tricks such as warm-up [7]. We adopt the linear learning rate decay strategy as default in the Transformers framework.
Table 5 shows the test accuracy results of the methods with different batch sizes. SNGM achieves the best performance for almost all batch size settings. | Figure 2 shows the learning curves of the five methods. We can observe that in the small-batch training, SNGM and other large-batch training methods achieve similar performance in terms of training loss and test accuracy as MSGD.
In large-batch training, SNGM achieves better training loss and test accuracy than the fou... | Furthermore, researchers in [19] argued that the extrapolation technique is suitable for large-batch training and proposed EXTRAP-SGD.
However, experimental implementations of these methods still require additional training tricks, such as warm-up, which may make the results inconsistent with the theory. | Many methods have been proposed for improving the performance of SGD with large batch sizes. The works in [7, 33]
proposed several tricks, such as warm-up and learning rate scaling schemes, to bridge the generalization gap under large-batch training settings. Researchers in [11] | C |
{\mathcal{F}}roman_support ( caligraphic_D ) ⊆ 2 start_POSTSUPERSCRIPT caligraphic_C end_POSTSUPERSCRIPT × blackboard_R start_POSTSUPERSCRIPT caligraphic_F end_POSTSUPERSCRIPT and, in the black-box setting, |𝒟|𝒟|\mathcal{D}|| caligraphic_D | may be uncountably infinite.
| The most general way to represent the scenario distribution 𝒟𝒟\mathcal{D}caligraphic_D is the black-box model [24, 12, 22, 19, 25], where we have access to an oracle to sample scenarios A𝐴Aitalic_A according to 𝒟𝒟\mathcal{D}caligraphic_D. We also consider the polynomial-scenarios model [23, 15, 21, 10], where the ... | Clustering is a fundamental task in unsupervised and self-supervised learning. The stochastic setting models situations in which decisions must be made in the presence of uncertainty and are of particular interest in learning and data science. The black-box model is motivated by data-driven applications where specific ... | The other three results are based on a reduction to a single-stage, deterministic robust outliers problem described in Section 4; namely, convert any ρ𝜌\rhoitalic_ρ-approximation algorithm for the robust outlier problem into a (ρ+2)𝜌2(\rho+2)( italic_ρ + 2 )-approximation algorithm for the corresponding two-stage sto... | Stochastic optimization, first introduced in the work of Beale [4] and Dantzig [8], provides a way to model uncertainty in the realization of the input data. In this paper, we give approximation algorithms for a family of problems in stochastic optimization, and more precisely in the 2222-stage recourse model [27].
Our... | B |
Both (sub)gradient noises and random graphs are considered in [11]-[13]. In [11], the local gradient noises are independent with bounded second-order moments and the graph sequence is i.i.d.
In [12]-[14], the (sub)gradient measurement noises are martingale difference sequences and their second-order conditional moments... | I. The local cost functions in this paper are not required to be differentiable and the subgradients only satisfy the linear growth condition.
The inner product of the subgradients and the error between local optimizers’ states and the global optimal solution inevitably exists in the recursive inequality of the conditi... | such as the economic dispatch in power grids ([1]) and the traffic flow control in intelligent transportation networks ([2]), et al. Considering the various uncertainties in practical network environments, distributed stochastic optimization algorithms have been widely studied. The (sub)gradients of local cost function... |
Motivated by distributed statistical learning over uncertain communication networks, we study the distributed stochastic convex optimization by networked local optimizers to cooperatively minimize a sum of local convex cost functions. The network is modeled by a sequence of time-varying random digraphs which may be sp... | In addition to uncertainties in information exchange, different assumptions on the cost functions have been discussed.
In the most of existing works on the distributed convex optimization, it is assumed that the subgradients are bounded if the local cost | D |
Typically, the attributes in microdata can be divided into three categories: (1) Explicit-Identifier (EI, also known as Personally-Identifiable Information), such as name and social security number, which can uniquely or mostly identify the record owner; (2) Quasi-Identifier (QI), such as age, gender and zip code, whi... | 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 similar ... | Generalization [8, 26] is one of the most widely used privacy-preserving techniques. It transforms the values on QI attributes into general forms, and the tuples with equally generalized values constitute an equivalence group. In this way, records in the same equivalence group are indistinguishable. k𝑘kitalic_k-Anonym... | However, despite protecting against both identity disclosure and attribute disclosure, the information loss of generalized table cannot be ignored. On the one hand, the generalized values are determined by only the maximum and the minimum QI values in equivalence groups, causing that the equivalence groups only preserv... |
Although the generalization for k𝑘kitalic_k-anonymity provides enough protection for identities, it is vulnerable to the attribute disclosure [23]. For instance, in Figure 1(b), the sensitive values in the third equivalence group are both “pneumonia”. Therefore, an adversary can infer the disease value of Dave by mat... | B |
In this section, we introduce our practice on three competitive segmentation methods including HTC, SOLOv2 and PointRend. We show step-by-step modifications adopted on PointRend, which achieves better performance and outputs much smoother instance boundaries than other methods.
| Deep learning has achieved great success in recent years Fan et al. (2019); Zhu et al. (2019); Luo et al. (2021, 2023); Chen et al. (2021). Recently, many modern instance segmentation approaches demonstrate outstanding performance on COCO and LVIS, such as HTC Chen et al. (2019a), SOLOv2 Wang et al. (2020), and PointRe... | Bells and Whistles. MaskRCNN-ResNet50 is used as baseline and it achieves 53.2 mAP. For PointRend, we follow the same setting as Kirillov et al. (2020) except for extracting both coarse and fine-grained features from the P2-P5 levels of FPN, rather than only P2 described in the paper. Surprisingly, PointRend yields 62.... | HTC is known as a competitive method for COCO and OpenImage. By enlarging the RoI size of both box and mask branches to 12 and 32 respectively for all three stages, we gain roughly 4 mAP improvement against the default settings in original paper. Mask scoring head Huang et al. (2019) adopted on the third stage gains an... |
Due to limited mask representation of HTC, we move on to SOLOv2, which utilizes much larger mask to segment objects. It builds an efficient yet simple instance segmentation framework, outperforming other segmentation methods like TensorMask Chen et al. (2019c), CondInst Tian et al. (2020) and BlendMask Chen et al. (20... | C |
For the significance of this conjecture we refer to the original paper [FK], and to Kalai’s blog [K] (embedded in Tao’s blog) which reports on all significant results concerning the conjecture. [KKLMS] establishes a weaker version of the conjecture. Its introduction is also a good source of information on the problem.
| We denote by εi:{−1,1}n→{−1,1}:subscript𝜀𝑖→superscript11𝑛11\varepsilon_{i}:\{-1,1\}^{n}\to\{-1,1\}italic_ε start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT : { - 1 , 1 } start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT → { - 1 , 1 } the projection onto the i𝑖iitalic_i-s coordinate: εi(δ1,…,δn)=δisubscript𝜀𝑖subsc... |
Here we give an embarrassingly simple presentation of an example of such a function (although it can be shown to be a version of the example in the previous version of this note). As was written in the previous version, an anonymous referee of version 1 wrote that the theorem was known to experts but not published. Ma... |
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. This solves a question raised by Gady Kozma s... | For the significance of this conjecture we refer to the original paper [FK], and to Kalai’s blog [K] (embedded in Tao’s blog) which reports on all significant results concerning the conjecture. [KKLMS] establishes a weaker version of the conjecture. Its introduction is also a good source of information on the problem.
| C |
Figure 1: Comparisons of different methods on cumulative reward under two different environments. The results are averaged over 10 trials and the error bars show the standard deviations. The environment changes abruptly in the left subfigure, whereas the environment changes gradually in the right subfigure. | For the case when the environment changes abruptly L𝐿Litalic_L times, our algorithm enjoys an O~(L1/3T2/3)~𝑂superscript𝐿13superscript𝑇23\tilde{O}(L^{1/3}T^{2/3})over~ start_ARG italic_O end_ARG ( italic_L start_POSTSUPERSCRIPT 1 / 3 end_POSTSUPERSCRIPT italic_T start_POSTSUPERSCRIPT 2 / 3 end_POSTSUPERSCRIPT ) dy... |
Figure 2 shows that the running times of LSVI-UCB-Restart and Ada-LSVI-UCB-Restart are roughly the same. They are much less compared with MASTER, OPT-WLSVI, LSVI-UCB, Epsilon-Greedy. This is because LSVI-UCB-Restart and Ada-LSVI-UCB-Restart can automatically restart according to the variation of the environment and th... | From Figure 1, we find that the restart strategy works better under abrupt changes than under gradual changes, since the gap between our algorithms and the baseline algorithms designed for stationary environments is larger in this setting. The reason is that the algorithms designed to explore in stationary MDPs are gen... | From Figure 1, we see LSVI-UCB-Restart with the knowledge of global variation drastically outperforms all other methods designed for stationary environments , in both abruptly-changing and gradually-changing environments, since it restarts the estimation of the Q𝑄Qitalic_Q function with knowledge of the total variatio... | C |
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