Paper Group ANR 749
Randomized Reactive Redundancy for Byzantine Fault-Tolerance in Parallelized Learning. Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs. Automatic Ensemble Learning for Online Influence Maximization. Learning retrosynthetic planning through self-play. Controlling Text Complexity in Neural Machine Translation. Triply R …
Randomized Reactive Redundancy for Byzantine Fault-Tolerance in Parallelized Learning
| Title | Randomized Reactive Redundancy for Byzantine Fault-Tolerance in Parallelized Learning |
| Authors | Nirupam Gupta, Nitin H. Vaidya |
| Abstract | This report considers the problem of Byzantine fault-tolerance in synchronous parallelized learning that is founded on the parallelized stochastic gradient descent (parallelized-SGD) algorithm. The system comprises a master, and $n$ workers, where up to $f$ of the workers are Byzantine faulty. Byzantine workers need not follow the master’s instructions correctly, and might send malicious incorrect (or faulty) information. The identity of the Byzantine workers remains fixed throughout the learning process, and is unknown a priori to the master. We propose two coding schemes, a deterministic scheme and a randomized scheme, for guaranteeing exact fault-tolerance if $2f < n$. The coding schemes use the concept of reactive redundancy for isolating Byzantine workers that eventually send faulty information. We note that the computation efficiencies of the schemes compare favorably with other (deterministic or randomized) coding schemes, for exact fault-tolerance. |
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| Published | 2019-12-19 |
| URL | https://arxiv.org/abs/1912.09528v1 |
| https://arxiv.org/pdf/1912.09528v1.pdf | |
| PWC | https://paperswithcode.com/paper/randomized-reactive-redundancy-for-byzantine |
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Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs
| Title | Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs |
| Authors | Khaled Koutini, Shreyan Chowdhury, Verena Haunschmid, Hamid Eghbal-zadeh, Gerhard Widmer |
| Abstract | We present CP-JKU submission to MediaEval 2019; a Receptive Field-(RF)-regularized and Frequency-Aware CNN approach for tagging music with emotion/mood labels. We perform an investigation regarding the impact of the RF of the CNNs on their performance on this dataset. We observe that ResNets with smaller receptive fields – originally adapted for acoustic scene classification – also perform well in the emotion tagging task. We improve the performance of such architectures using techniques such as Frequency Awareness and Shake-Shake regularization, which were used in previous work on general acoustic recognition tasks. |
| Tasks | Acoustic Scene Classification, Scene Classification |
| Published | 2019-10-28 |
| URL | https://arxiv.org/abs/1911.05833v1 |
| https://arxiv.org/pdf/1911.05833v1.pdf | |
| PWC | https://paperswithcode.com/paper/emotion-and-theme-recognition-in-music-with |
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Automatic Ensemble Learning for Online Influence Maximization
| Title | Automatic Ensemble Learning for Online Influence Maximization |
| Authors | Xiaojin Zhang |
| Abstract | We consider the problem of selecting a seed set to maximize the expected number of influenced nodes in the social network, referred to as the \textit{influence maximization} (IM) problem. We assume that the topology of the social network is prescribed while the influence probabilities among edges are unknown. In order to learn the influence probabilities and simultaneously maximize the influence spread, we consider the tradeoff between exploiting the current estimation of the influence probabilities to ensure certain influence spread and exploring more nodes to learn better about the influence probabilities. The exploitation-exploration trade-off is the core issue in the multi-armed bandit (MAB) problem. If we regard the influence spread as the reward, then the IM problem could be reduced to the combinatorial multi-armed bandits. At each round, the learner selects a limited number of seed nodes in the social network, then the influence spreads over the network according to the real influence probabilities. The learner could observe the activation status of the edge if and only if its start node is influenced, which is referred to as the edge-level semi-bandit feedback. Two classical bandit algorithms including Thompson Sampling and Epsilon Greedy are used to solve this combinatorial problem. To ensure the robustness of these two algorithms, we use an automatic ensemble learning strategy, which combines the exploration strategy with exploitation strategy. The ensemble algorithm is self-adaptive regarding that the probability of each algorithm could be adjusted based on the historical performance of the algorithm. Experimental evaluation illustrates the effectiveness of the automatically adjusted hybridization of exploration algorithm with exploitation algorithm. |
| Tasks | Multi-Armed Bandits |
| Published | 2019-11-25 |
| URL | https://arxiv.org/abs/1911.10728v1 |
| https://arxiv.org/pdf/1911.10728v1.pdf | |
| PWC | https://paperswithcode.com/paper/automatic-ensemble-learning-for-online |
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Learning retrosynthetic planning through self-play
| Title | Learning retrosynthetic planning through self-play |
| Authors | John S. Schreck, Connor W. Coley, Kyle J. M. Bishop |
| Abstract | The problem of retrosynthetic planning can be framed as one player game, in which the chemist (or a computer program) works backwards from a molecular target to simpler starting materials though a series of choices regarding which reactions to perform. This game is challenging as the combinatorial space of possible choices is astronomical, and the value of each choice remains uncertain until the synthesis plan is completed and its cost evaluated. Here, we address this problem using deep reinforcement learning to identify policies that make (near) optimal reaction choices during each step of retrosynthetic planning. Using simulated experience or self-play, we train neural networks to estimate the expected synthesis cost or value of any given molecule based on a representation of its molecular structure. We show that learned policies based on this value network outperform heuristic approaches in synthesizing unfamiliar molecules from available starting materials using the fewest number of reactions. We discuss how the learned policies described here can be incorporated into existing synthesis planning tools and how they can be adapted to changes in the synthesis cost objective or material availability. |
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| Published | 2019-01-19 |
| URL | http://arxiv.org/abs/1901.06569v1 |
| http://arxiv.org/pdf/1901.06569v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-retrosynthetic-planning-through-self |
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Controlling Text Complexity in Neural Machine Translation
| Title | Controlling Text Complexity in Neural Machine Translation |
| Authors | Sweta Agrawal, Marine Carpuat |
| Abstract | This work introduces a machine translation task where the output is aimed at audiences of different levels of target language proficiency. We collect a high quality dataset of news articles available in English and Spanish, written for diverse grade levels and propose a method to align segments across comparable bilingual articles. The resulting dataset makes it possible to train multi-task sequence-to-sequence models that translate Spanish into English targeted at an easier reading grade level than the original Spanish. We show that these multi-task models outperform pipeline approaches that translate and simplify text independently. |
| Tasks | Machine Translation |
| Published | 2019-11-03 |
| URL | https://arxiv.org/abs/1911.00835v1 |
| https://arxiv.org/pdf/1911.00835v1.pdf | |
| PWC | https://paperswithcode.com/paper/controlling-text-complexity-in-neural-machine-1 |
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Triply Robust Off-Policy Evaluation
| Title | Triply Robust Off-Policy Evaluation |
| Authors | Anqi Liu, Hao Liu, Anima Anandkumar, Yisong Yue |
| Abstract | We propose a robust regression approach to off-policy evaluation (OPE) for contextual bandits. We frame OPE as a covariate-shift problem and leverage modern robust regression tools. Ours is a general approach that can be used to augment any existing OPE method that utilizes the direct method. When augmenting doubly robust methods, we call the resulting method Triply Robust. We prove upper bounds on the resulting bias and variance, as well as derive novel minimax bounds based on robust minimax analysis for covariate shift. Our robust regression method is compatible with deep learning, and is thus applicable to complex OPE settings that require powerful function approximators. Finally, we demonstrate superior empirical performance across the standard OPE benchmarks, especially in the case where the logging policy is unknown and must be estimated from data. |
| Tasks | Multi-Armed Bandits |
| Published | 2019-11-13 |
| URL | https://arxiv.org/abs/1911.05811v2 |
| https://arxiv.org/pdf/1911.05811v2.pdf | |
| PWC | https://paperswithcode.com/paper/triply-robust-off-policy-evaluation |
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Incentivized Exploration for Multi-Armed Bandits under Reward Drift
| Title | Incentivized Exploration for Multi-Armed Bandits under Reward Drift |
| Authors | Zhiyuan Liu, Huazheng Wang, Fan Shen, Kai Liu, Lijun Chen |
| Abstract | We study incentivized exploration for the multi-armed bandit (MAB) problem where the players receive compensation for exploring arms other than the greedy choice and may provide biased feedback on reward. We seek to understand the impact of this drifted reward feedback by analyzing the performance of three instantiations of the incentivized MAB algorithm: UCB, $\varepsilon$-Greedy, and Thompson Sampling. Our results show that they all achieve $\mathcal{O}(\log T)$ regret and compensation under the drifted reward, and are therefore effective in incentivizing exploration. Numerical examples are provided to complement the theoretical analysis. |
| Tasks | Multi-Armed Bandits |
| Published | 2019-11-12 |
| URL | https://arxiv.org/abs/1911.05142v3 |
| https://arxiv.org/pdf/1911.05142v3.pdf | |
| PWC | https://paperswithcode.com/paper/incentivized-exploration-for-multi-armed |
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Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control
| Title | Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control |
| Authors | Gurcan Comert, Zadid Khan, Mizanur Rahman, Mashrur Chowdhury |
| Abstract | Traffic congestion at a signalized intersection greatly reduces the travel time reliability in urban areas. Adaptive signal control system (ASCS) is the most advanced traffic signal technology that regulates the signal phasing and timings considering the patterns in real-time in order to reduce congestion. Real-time prediction of queue lengths can be used to adjust the phasing and timings for different movements at an intersection with ASCS. The accuracy of the prediction varies based on the factors, such as the stochastic nature of the vehicle arrival rates, time of the day, weather and driver characteristics. In addition, accurate prediction for multilane, undersaturated and saturated traffic scenarios is challenging. Thus, the objective of this study is to develop queue length prediction models for signalized intersections that can be leveraged by ASCS using four variations of Grey systems: (i) the first order single variable Grey model (GM(1,1)); (ii) GM(1,1) with Fourier error corrections; (iii) the Grey Verhulst model (GVM), and (iv) GVM with Fourier error corrections. The efficacy of the GM is that they facilitate fast processing; as these models do not require a large amount of data; as would be needed in artificial intelligence models; and they are able to adapt to stochastic changes, unlike statistical models. We have conducted a case study using queue length data from five intersections with ASCS on a calibrated roadway network in Lexington, South Carolina. GM were compared with linear, nonlinear time series models, and long short-term memory (LSTM) neural network. Based on our analyses, we found that EGVM reduces the prediction error over closest competing models (i.e., LSTM and time series models) in predicting average and maximum queue lengths by 40% and 42%, respectively, in terms of Root Mean Squared Error, and 51% and 50%, respectively, in terms of Mean Absolute Error. |
| Tasks | Time Series |
| Published | 2019-12-29 |
| URL | https://arxiv.org/abs/1912.12676v1 |
| https://arxiv.org/pdf/1912.12676v1.pdf | |
| PWC | https://paperswithcode.com/paper/grey-models-for-short-term-queue-length |
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Coupling techniques for nonlinear ensemble filtering
| Title | Coupling techniques for nonlinear ensemble filtering |
| Authors | Alessio Spantini, Ricardo Baptista, Youssef Marzouk |
| Abstract | We consider filtering in high-dimensional non-Gaussian state-space models with intractable transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in space and time. We propose a novel filtering methodology that harnesses transportation of measures, convex optimization, and ideas from probabilistic graphical models to yield robust ensemble approximations of the filtering distribution in high dimensions. Our approach can be understood as the natural generalization of the ensemble Kalman filter (EnKF) to nonlinear updates, using stochastic or deterministic couplings. The use of nonlinear updates can reduce the intrinsic bias of the EnKF at a marginal increase in computational cost. We avoid any form of importance sampling and introduce non-Gaussian localization approaches for dimension scalability. Our framework achieves state-of-the-art tracking performance on challenging configurations of the Lorenz-96 model in the chaotic regime. |
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| Published | 2019-06-30 |
| URL | https://arxiv.org/abs/1907.00389v1 |
| https://arxiv.org/pdf/1907.00389v1.pdf | |
| PWC | https://paperswithcode.com/paper/coupling-techniques-for-nonlinear-ensemble |
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Alternative Techniques for Mapping Paths to HLAI
| Title | Alternative Techniques for Mapping Paths to HLAI |
| Authors | Ross Gruetzemacher, David Paradice |
| Abstract | The only systematic mapping of the HLAI technical landscape was conducted at a workshop in 2009 [Adams et al., 2012]. However, the results from it were not what organizers had hoped for [Goertzel 2014, 2016], merely just a series of milestones, up to 50% of which could be argued to have been completed already. We consider two more recent articles outlining paths to human-like intelligence [Mikolov et al., 2016; Lake et al., 2017]. These offer technical and more refined assessments of the requirements for HLAI rather than just milestones. While useful, they also have limitations. To address these limitations we propose the use of alternative techniques for an updated systematic mapping of the paths to HLAI. The newly proposed alternative techniques can model complex paths of future technologies using intricate directed graphs. Specifically, there are two classes of alternative techniques that we consider: scenario mapping methods and techniques for eliciting expert opinion through digital platforms and crowdsourcing. We assess the viability and utility of both the previous and alternative techniques, finding that the proposed alternative techniques could be very beneficial in advancing the existing body of knowledge on the plausible frameworks for creating HLAI. In conclusion, we encourage discussion and debate to initiate efforts to use these proposed techniques for mapping paths to HLAI. |
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| Published | 2019-05-02 |
| URL | http://arxiv.org/abs/1905.00614v1 |
| http://arxiv.org/pdf/1905.00614v1.pdf | |
| PWC | https://paperswithcode.com/paper/alternative-techniques-for-mapping-paths-to |
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On linear convergence of two decentralized algorithms
| Title | On linear convergence of two decentralized algorithms |
| Authors | Yao Li, Ming Yan |
| Abstract | Decentralized algorithms solve multi-agent problems over a connected network, where the information can only be exchanged with accessible neighbors. Though there exist several decentralized optimization algorithms, there are still gaps in convergence conditions and rates between decentralized algorithms and centralized ones. In this paper, we fill some gaps by considering two decentralized consensus algorithms: EXTRA and NIDS. Both algorithms converge linearly with strongly convex functions. We will answer two questions regarding both algorithms. What are the optimal upper bounds for their stepsizes? Do decentralized algorithms require more properties on the functions for linear convergence than centralized ones? More specifically, we relax the required conditions for linear convergence for both algorithms. For EXTRA, we show that the stepsize is in order of $O(\frac{1}{L})$ ($L$ is the Lipschitz constant of the gradient of the functions), which is comparable to that of centralized algorithms, though the upper bound is still smaller than that of centralized ones. For NIDS, we show that the upper bound of the stepsize is the same as that of centralized ones, and it does not depend on the network. In addition, we relax the requirement for the functions and the mixing matrix, which reflects the topology of the network. As far as we know, we provide the linear convergence results for both algorithms under the weakest conditions. |
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| Published | 2019-06-17 |
| URL | https://arxiv.org/abs/1906.07225v1 |
| https://arxiv.org/pdf/1906.07225v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-linear-convergence-of-two-decentralized |
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Sparse $\ell_1$ and $\ell_2$ Center Classifiers
| Title | Sparse $\ell_1$ and $\ell_2$ Center Classifiers |
| Authors | Giuseppe C. Calafiore, Giulia Fracastoro |
| Abstract | The nearest-centroid classifier is a simple linear-time classifier based on computing the centroids of the data classes in the training phase, and then assigning a new datum to the class corresponding to its nearest centroid. Thanks to its very low computational cost, the nearest-centroid classifier is still widely used in machine learning, despite the development of many other more sophisticated classification methods. In this paper, we propose two sparse variants of the nearest-centroid classifier, based respectively on $\ell_1$ and $\ell_2$ distance criteria. The proposed sparse classifiers perform simultaneous classification and feature selection, by detecting the features that are most relevant for the classification purpose. We show that training of the proposed sparse models, with both distance criteria, can be performed exactly (i.e., the globally optimal set of features is selected) and at a quasi-linear computational cost. The experimental results show that the proposed methods are competitive in accuracy with state-of-the-art feature selection techniques, while having a significantly lower computational cost. |
| Tasks | Feature Selection |
| Published | 2019-11-17 |
| URL | https://arxiv.org/abs/1911.07320v2 |
| https://arxiv.org/pdf/1911.07320v2.pdf | |
| PWC | https://paperswithcode.com/paper/sparse-ell_1-and-ell_2-center-classifiers |
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Evidence Sentence Extraction for Machine Reading Comprehension
| Title | Evidence Sentence Extraction for Machine Reading Comprehension |
| Authors | Hai Wang, Dian Yu, Kai Sun, Jianshu Chen, Dong Yu, David McAllester, Dan Roth |
| Abstract | Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks. However, it is still difficult to interpret the predictions of existing MRC models. In this paper, we focus on extracting evidence sentences that can explain or support the answers of multiple-choice MRC tasks, where the majority of answer options cannot be directly extracted from reference documents. Due to the lack of ground truth evidence sentence labels in most cases, we apply distant supervision to generate imperfect labels and then use them to train an evidence sentence extractor. To denoise the noisy labels, we apply a recently proposed deep probabilistic logic learning framework to incorporate both sentence-level and cross-sentence linguistic indicators for indirect supervision. We feed the extracted evidence sentences into existing MRC models and evaluate the end-to-end performance on three challenging multiple-choice MRC datasets: MultiRC, RACE, and DREAM, achieving comparable or better performance than the same models that take as input the full reference document. To the best of our knowledge, this is the first work extracting evidence sentences for multiple-choice MRC. |
| Tasks | Machine Reading Comprehension, Open-Domain Question Answering, Question Answering, Reading Comprehension |
| Published | 2019-02-23 |
| URL | https://arxiv.org/abs/1902.08852v2 |
| https://arxiv.org/pdf/1902.08852v2.pdf | |
| PWC | https://paperswithcode.com/paper/evidence-sentence-extraction-for-machine |
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VRSTC: Occlusion-Free Video Person Re-Identification
| Title | VRSTC: Occlusion-Free Video Person Re-Identification |
| Authors | Ruibing Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan, Xilin Chen |
| Abstract | Video person re-identification (re-ID) plays an important role in surveillance video analysis. However, the performance of video re-ID degenerates severely under partial occlusion. In this paper, we propose a novel network, called Spatio-Temporal Completion network (STCnet), to explicitly handle partial occlusion problem. Different from most previous works that discard the occluded frames, STCnet can recover the appearance of the occluded parts. For one thing, the spatial structure of a pedestrian frame can be used to predict the occluded body parts from the unoccluded body parts of this frame. For another, the temporal patterns of pedestrian sequence provide important clues to generate the contents of occluded parts. With the Spatio-temporal information, STCnet can recover the appearance for the occluded parts, which could be leveraged with those unoccluded parts for more accurate video re-ID. By combining a re-ID network with STCnet, a video re-ID framework robust to partial occlusion (VRSTC) is proposed. Experiments on three challenging video re-ID databases demonstrate that the proposed approach outperforms the state-of-the-art. |
| Tasks | Person Re-Identification, Video-Based Person Re-Identification |
| Published | 2019-07-19 |
| URL | https://arxiv.org/abs/1907.08427v1 |
| https://arxiv.org/pdf/1907.08427v1.pdf | |
| PWC | https://paperswithcode.com/paper/vrstc-occlusion-free-video-person-re-1 |
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Dual Co-Matching Network for Multi-choice Reading Comprehension
| Title | Dual Co-Matching Network for Multi-choice Reading Comprehension |
| Authors | Shuailiang Zhang, Hai Zhao, Yuwei Wu, Zhuosheng Zhang, Xi Zhou, Xiang Zhou |
| Abstract | Multi-choice reading comprehension is a challenging task that requires complex reasoning procedure. Given passage and question, a correct answer need to be selected from a set of candidate answers. In this paper, we propose \textbf{D}ual \textbf{C}o-\textbf{M}atching \textbf{N}etwork (\textbf{DCMN}) which model the relationship among passage, question and answer bidirectionally. Different from existing approaches which only calculate question-aware or option-aware passage representation, we calculate passage-aware question representation and passage-aware answer representation at the same time. To demonstrate the effectiveness of our model, we evaluate our model on a large-scale multiple choice machine reading comprehension dataset (i.e. RACE). Experimental result show that our proposed model achieves new state-of-the-art results. |
| Tasks | Machine Reading Comprehension, Reading Comprehension |
| Published | 2019-01-27 |
| URL | https://arxiv.org/abs/1901.09381v2 |
| https://arxiv.org/pdf/1901.09381v2.pdf | |
| PWC | https://paperswithcode.com/paper/dual-co-matching-network-for-multi-choice |
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