Paper Group ANR 536
A Parallel Best-Response Algorithm with Exact Line Search for Nonconvex Sparsity-Regularized Rank Minimization. Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations. False arrhythmia alarm reduction in the intensive care unit. Developing a concept-level knowledge base for sen …
A Parallel Best-Response Algorithm with Exact Line Search for Nonconvex Sparsity-Regularized Rank Minimization
| Title | A Parallel Best-Response Algorithm with Exact Line Search for Nonconvex Sparsity-Regularized Rank Minimization |
| Authors | Yang Yang, Marius Pesavento |
| Abstract | In this paper, we propose a convergent parallel best-response algorithm with the exact line search for the nondifferentiable nonconvex sparsity-regularized rank minimization problem. On the one hand, it exhibits a faster convergence than subgradient algorithms and block coordinate descent algorithms. On the other hand, its convergence to a stationary point is guaranteed, while ADMM algorithms only converge for convex problems. Furthermore, the exact line search procedure in the proposed algorithm is performed efficiently in closed-form to avoid the meticulous choice of stepsizes, which is however a common bottleneck in subgradient algorithms and successive convex approximation algorithms. Finally, the proposed algorithm is numerically tested. |
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| Published | 2017-11-13 |
| URL | http://arxiv.org/abs/1711.04489v1 |
| http://arxiv.org/pdf/1711.04489v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-parallel-best-response-algorithm-with-exact |
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Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations
| Title | Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations |
| Authors | Marcel Nonnenmacher, Srinivas C. Turaga, Jakob H. Macke |
| Abstract | A powerful approach for understanding neural population dynamics is to extract low-dimensional trajectories from population recordings using dimensionality reduction methods. Current approaches for dimensionality reduction on neural data are limited to single population recordings, and can not identify dynamics embedded across multiple measurements. We propose an approach for extracting low-dimensional dynamics from multiple, sequential recordings. Our algorithm scales to data comprising millions of observed dimensions, making it possible to access dynamics distributed across large populations or multiple brain areas. Building on subspace-identification approaches for dynamical systems, we perform parameter estimation by minimizing a moment-matching objective using a scalable stochastic gradient descent algorithm: The model is optimized to predict temporal covariations across neurons and across time. We show how this approach naturally handles missing data and multiple partial recordings, and can identify dynamics and predict correlations even in the presence of severe subsampling and small overlap between recordings. We demonstrate the effectiveness of the approach both on simulated data and a whole-brain larval zebrafish imaging dataset. |
| Tasks | Dimensionality Reduction |
| Published | 2017-11-06 |
| URL | http://arxiv.org/abs/1711.01847v1 |
| http://arxiv.org/pdf/1711.01847v1.pdf | |
| PWC | https://paperswithcode.com/paper/extracting-low-dimensional-dynamics-from |
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False arrhythmia alarm reduction in the intensive care unit
| Title | False arrhythmia alarm reduction in the intensive care unit |
| Authors | Andrea S. Li, Alistair E. W. Johnson, Roger G. Mark |
| Abstract | Research has shown that false alarms constitute more than 80% of the alarms triggered in the intensive care unit (ICU). The high false arrhythmia alarm rate has severe implications such as disruption of patient care, caregiver alarm fatigue, and desensitization from clinical staff to real life-threatening alarms. A method to reduce the false alarm rate would therefore greatly benefit patients as well as nurses in their ability to provide care. We here develop and describe a robust false arrhythmia alarm reduction system for use in the ICU. Building off of work previously described in the literature, we make use of signal processing and machine learning techniques to identify true and false alarms for five arrhythmia types. This baseline algorithm alone is able to perform remarkably well, with a sensitivity of 0.908, a specificity of 0.838, and a PhysioNet/CinC challenge score of 0.756. We additionally explore dynamic time warping techniques on both the entire alarm signal as well as on a beat-by-beat basis in an effort to improve performance of ventricular tachycardia, which has in the literature been one of the hardest arrhythmias to classify. Such an algorithm with strong performance and efficiency could potentially be translated for use in the ICU to promote overall patient care and recovery. |
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| Published | 2017-09-11 |
| URL | http://arxiv.org/abs/1709.03562v1 |
| http://arxiv.org/pdf/1709.03562v1.pdf | |
| PWC | https://paperswithcode.com/paper/false-arrhythmia-alarm-reduction-in-the |
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Developing a concept-level knowledge base for sentiment analysis in Singlish
| Title | Developing a concept-level knowledge base for sentiment analysis in Singlish |
| Authors | Rajiv Bajpai, Soujanya Poria, Danyun Ho, Erik Cambria |
| Abstract | In this paper, we present Singlish sentiment lexicon, a concept-level knowledge base for sentiment analysis that associates multiword expressions to a set of emotion labels and a polarity value. Unlike many other sentiment analysis resources, this lexicon is not built by manually labeling pieces of knowledge coming from general NLP resources such as WordNet or DBPedia. Instead, it is automatically constructed by applying graph-mining and multi-dimensional scaling techniques on the affective common-sense knowledge collected from three different sources. This knowledge is represented redundantly at three levels: semantic network, matrix, and vector space. Subsequently, the concepts are labeled by emotions and polarity through the ensemble application of spreading activation, neural networks and an emotion categorization model. |
| Tasks | Common Sense Reasoning, Sentiment Analysis |
| Published | 2017-07-14 |
| URL | http://arxiv.org/abs/1707.04408v1 |
| http://arxiv.org/pdf/1707.04408v1.pdf | |
| PWC | https://paperswithcode.com/paper/developing-a-concept-level-knowledge-base-for |
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A simulated annealing approach to optimal storing in a multi-level warehouse
| Title | A simulated annealing approach to optimal storing in a multi-level warehouse |
| Authors | Alexander Eckrot, Carina Geldhauser, Jan Jurczyk |
| Abstract | We propose a simulated annealing algorithm specifically tailored to optimise total retrieval times in a multi-level warehouse under complex pre-batched picking constraints. Experiments on real data from a picker-to-parts order picking process in the warehouse of a European manufacturer show that optimal storage assignments do not necessarily display features presumed in heuristics, such as clustering of positively correlated items or ordering of items by picking frequency. In an experiment run on more than 4000 batched orders with 1 to 150 items per batch, the storage assignment suggested by the algorithm produces a 21% reduction in the total retrieval time with respect to a frequency-based storage assignment. |
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| Published | 2017-03-25 |
| URL | http://arxiv.org/abs/1704.01049v1 |
| http://arxiv.org/pdf/1704.01049v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-simulated-annealing-approach-to-optimal |
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GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery
| Title | GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery |
| Authors | Seungkyun Hong, Seongchan Kim, Minsu Joh, Sa-kwang Song |
| Abstract | Advances in remote sensing technologies have made it possible to use high-resolution visual data for weather observation and forecasting tasks. We propose the use of multi-layer neural networks for understanding complex atmospheric dynamics based on multichannel satellite images. The capability of our model was evaluated by using a linear regression task for single typhoon coordinates prediction. A specific combination of models and different activation policies enabled us to obtain an interesting prediction result in the northeastern hemisphere (ENH). |
| Tasks | Eye Tracking |
| Published | 2017-08-11 |
| URL | http://arxiv.org/abs/1708.03417v1 |
| http://arxiv.org/pdf/1708.03417v1.pdf | |
| PWC | https://paperswithcode.com/paper/globenet-convolutional-neural-networks-for |
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Efficient Large Scale Clustering based on Data Partitioning
| Title | Efficient Large Scale Clustering based on Data Partitioning |
| Authors | Malika Bendechache, Nhien-An Le-Khac, M-Tahar Kechadi |
| Abstract | Clustering techniques are very attractive for extracting and identifying patterns in datasets. However, their application to very large spatial datasets presents numerous challenges such as high-dimensionality data, heterogeneity, and high complexity of some algorithms. For instance, some algorithms may have linear complexity but they require the domain knowledge in order to determine their input parameters. Distributed clustering techniques constitute a very good alternative to the big data challenges (e.g.,Volume, Variety, Veracity, and Velocity). Usually these techniques consist of two phases. The first phase generates local models or patterns and the second one tends to aggregate the local results to obtain global models. While the first phase can be executed in parallel on each site and, therefore, efficient, the aggregation phase is complex, time consuming and may produce incorrect and ambiguous global clusters and therefore incorrect models. In this paper we propose a new distributed clustering approach to deal efficiently with both phases, generation of local results and generation of global models by aggregation. For the first phase, our approach is capable of analysing the datasets located in each site using different clustering techniques. The aggregation phase is designed in such a way that the final clusters are compact and accurate while the overall process is efficient in time and memory allocation. For the evaluation, we use two well-known clustering algorithms, K-Means and DBSCAN. One of the key outputs of this distributed clustering technique is that the number of global clusters is dynamic, no need to be fixed in advance. Experimental results show that the approach is scalable and produces high quality results. |
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| Published | 2017-04-11 |
| URL | http://arxiv.org/abs/1704.03421v2 |
| http://arxiv.org/pdf/1704.03421v2.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-large-scale-clustering-based-on |
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Experience Replay Using Transition Sequences
| Title | Experience Replay Using Transition Sequences |
| Authors | Thommen George Karimpanal, Roland Bouffanais |
| Abstract | Experience replay is one of the most commonly used approaches to improve the sample efficiency of reinforcement learning algorithms. In this work, we propose an approach to select and replay sequences of transitions in order to accelerate the learning of a reinforcement learning agent in an off-policy setting. In addition to selecting appropriate sequences, we also artificially construct transition sequences using information gathered from previous agent-environment interactions. These sequences, when replayed, allow value function information to trickle down to larger sections of the state/state-action space, thereby making the most of the agent’s experience. We demonstrate our approach on modified versions of standard reinforcement learning tasks such as the mountain car and puddle world problems and empirically show that it enables better learning of value functions as compared to other forms of experience replay. Further, we briefly discuss some of the possible extensions to this work, as well as applications and situations where this approach could be particularly useful. |
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| Published | 2017-05-30 |
| URL | https://arxiv.org/abs/1705.10834v2 |
| https://arxiv.org/pdf/1705.10834v2.pdf | |
| PWC | https://paperswithcode.com/paper/experience-replay-using-transition-sequences |
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Mass Volume Curves and Anomaly Ranking
| Title | Mass Volume Curves and Anomaly Ranking |
| Authors | Stephan Clémençon, Albert Thomas |
| Abstract | This paper aims at formulating the issue of ranking multivariate unlabeled observations depending on their degree of abnormality as an unsupervised statistical learning task. In the 1-d situation, this problem is usually tackled by means of tail estimation techniques: univariate observations are viewed as all the more abnormal' as they are located far in the tail(s) of the underlying probability distribution. It would be desirable as well to dispose of a scalar valued scoring’ function allowing for comparing the degree of abnormality of multivariate observations. Here we formulate the issue of scoring anomalies as a M-estimation problem by means of a novel functional performance criterion, referred to as the Mass Volume curve (MV curve in short), whose optimal elements are strictly increasing transforms of the density almost everywhere on the support of the density. We first study the statistical estimation of the MV curve of a given scoring function and we provide a strategy to build confidence regions using a smoothed bootstrap approach. Optimization of this functional criterion over the set of piecewise constant scoring functions is next tackled. This boils down to estimating a sequence of empirical minimum volume sets whose levels are chosen adaptively from the data, so as to adjust to the variations of the optimal MV curve, while controling the bias of its approximation by a stepwise curve. Generalization bounds are then established for the difference in sup norm between the MV curve of the empirical scoring function thus obtained and the optimal MV curve. |
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| Published | 2017-05-03 |
| URL | http://arxiv.org/abs/1705.01305v2 |
| http://arxiv.org/pdf/1705.01305v2.pdf | |
| PWC | https://paperswithcode.com/paper/mass-volume-curves-and-anomaly-ranking |
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Semantic-driven Generation of Hyperlapse from $360^\circ$ Video
| Title | Semantic-driven Generation of Hyperlapse from $360^\circ$ Video |
| Authors | Wei-Sheng Lai, Yujia Huang, Neel Joshi, Chris Buehler, Ming-Hsuan Yang, Sing Bing Kang |
| Abstract | We present a system for converting a fully panoramic ($360^\circ$) video into a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience. Our system exploits visual saliency and semantics to non-uniformly sample in space and time for generating hyperlapses. In addition, users can optionally choose objects of interest for customizing the hyperlapses. We first stabilize an input $360^\circ$ video by smoothing the rotation between adjacent frames and then compute regions of interest and saliency scores. An initial hyperlapse is generated by optimizing the saliency and motion smoothness followed by the saliency-aware frame selection. We further smooth the result using an efficient 2D video stabilization approach that adaptively selects the motion model to generate the final hyperlapse. We validate the design of our system by showing results for a variety of scenes and comparing against the state-of-the-art method through a user study. |
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| Published | 2017-03-31 |
| URL | http://arxiv.org/abs/1703.10798v4 |
| http://arxiv.org/pdf/1703.10798v4.pdf | |
| PWC | https://paperswithcode.com/paper/semantic-driven-generation-of-hyperlapse-from |
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A Joint Framework for Argumentative Text Analysis Incorporating Domain Knowledge
| Title | A Joint Framework for Argumentative Text Analysis Incorporating Domain Knowledge |
| Authors | Zhongyu Wei, Chen Li, Yang Liu |
| Abstract | For argumentation mining, there are several sub-tasks such as argumentation component type classification, relation classification. Existing research tends to solve such sub-tasks separately, but ignore the close relation between them. In this paper, we present a joint framework incorporating logical relation between sub-tasks to improve the performance of argumentation structure generation. We design an objective function to combine the predictions from individual models for each sub-task and solve the problem with some constraints constructed from background knowledge. We evaluate our proposed model on two public corpora and the experiment results show that our model can outperform the baseline that uses a separate model significantly for each sub-task. Our model also shows advantages on component-related sub-tasks compared to a state-of-the-art joint model based on the evidence graph. |
| Tasks | Relation Classification |
| Published | 2017-01-19 |
| URL | http://arxiv.org/abs/1701.05343v1 |
| http://arxiv.org/pdf/1701.05343v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-joint-framework-for-argumentative-text |
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Underdamped Langevin MCMC: A non-asymptotic analysis
| Title | Underdamped Langevin MCMC: A non-asymptotic analysis |
| Authors | Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, Michael I. Jordan |
| Abstract | We study the underdamped Langevin diffusion when the log of the target distribution is smooth and strongly concave. We present a MCMC algorithm based on its discretization and show that it achieves $\varepsilon$ error (in 2-Wasserstein distance) in $\mathcal{O}(\sqrt{d}/\varepsilon)$ steps. This is a significant improvement over the best known rate for overdamped Langevin MCMC, which is $\mathcal{O}(d/\varepsilon^2)$ steps under the same smoothness/concavity assumptions. The underdamped Langevin MCMC scheme can be viewed as a version of Hamiltonian Monte Carlo (HMC) which has been observed to outperform overdamped Langevin MCMC methods in a number of application areas. We provide quantitative rates that support this empirical wisdom. |
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| Published | 2017-07-12 |
| URL | http://arxiv.org/abs/1707.03663v7 |
| http://arxiv.org/pdf/1707.03663v7.pdf | |
| PWC | https://paperswithcode.com/paper/underdamped-langevin-mcmc-a-non-asymptotic |
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Inverse Reinforce Learning with Nonparametric Behavior Clustering
| Title | Inverse Reinforce Learning with Nonparametric Behavior Clustering |
| Authors | Siddharthan Rajasekaran, Jinwei Zhang, Jie Fu |
| Abstract | Inverse Reinforcement Learning (IRL) is the task of learning a single reward function given a Markov Decision Process (MDP) without defining the reward function, and a set of demonstrations generated by humans/experts. However, in practice, it may be unreasonable to assume that human behaviors can be explained by one reward function since they may be inherently inconsistent. Also, demonstrations may be collected from various users and aggregated to infer and predict user’s behaviors. In this paper, we introduce the Non-parametric Behavior Clustering IRL algorithm to simultaneously cluster demonstrations and learn multiple reward functions from demonstrations that may be generated from more than one behaviors. Our method is iterative: It alternates between clustering demonstrations into different behavior clusters and inverse learning the reward functions until convergence. It is built upon the Expectation-Maximization formulation and non-parametric clustering in the IRL setting. Further, to improve the computation efficiency, we remove the need of completely solving multiple IRL problems for multiple clusters during the iteration steps and introduce a resampling technique to avoid generating too many unlikely clusters. We demonstrate the convergence and efficiency of the proposed method through learning multiple driver behaviors from demonstrations generated from a grid-world environment and continuous trajectories collected from autonomous robot cars using the Gazebo robot simulator. |
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| Published | 2017-12-15 |
| URL | http://arxiv.org/abs/1712.05514v1 |
| http://arxiv.org/pdf/1712.05514v1.pdf | |
| PWC | https://paperswithcode.com/paper/inverse-reinforce-learning-with-nonparametric |
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Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics
| Title | Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics |
| Authors | Yanning Shen, Tianyi Chen, Georgios B. Giannakis |
| Abstract | Kernel-based methods exhibit well-documented performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. Especially when the latter is not available, multi-kernel learning has gained popularity thanks to its flexibility in choosing kernels from a prescribed kernel dictionary. Leveraging the random feature approximation and its recent orthogonality-promoting variant, the present contribution develops a scalable multi-kernel learning scheme (termed Raker) to obtain the sought nonlinear learning function `on the fly,’ first for static environments. To further boost performance in dynamic environments, an adaptive multi-kernel learning scheme (termed AdaRaker) is developed. AdaRaker accounts not only for data-driven learning of kernel combination, but also for the unknown dynamics. Performance is analyzed in terms of both static and dynamic regrets. AdaRaker is uniquely capable of tracking nonlinear learning functions in environments with unknown dynamics, and with with analytic performance guarantees. Tests with synthetic and real datasets are carried out to showcase the effectiveness of the novel algorithms. | |
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| Published | 2017-12-28 |
| URL | http://arxiv.org/abs/1712.09983v3 |
| http://arxiv.org/pdf/1712.09983v3.pdf | |
| PWC | https://paperswithcode.com/paper/random-feature-based-online-multi-kernel |
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Complexity Metric for Code-Mixed Social Media Text
| Title | Complexity Metric for Code-Mixed Social Media Text |
| Authors | Souvick Ghosh, Satanu Ghosh, Dipankar Das |
| Abstract | An evaluation metric is an absolute necessity for measuring the performance of any system and complexity of any data. In this paper, we have discussed how to determine the level of complexity of code-mixed social media texts that are growing rapidly due to multilingual interference. In general, texts written in multiple languages are often hard to comprehend and analyze. At the same time, in order to meet the demands of analysis, it is also necessary to determine the complexity of a particular document or a text segment. Thus, in the present paper, we have discussed the existing metrics for determining the code-mixing complexity of a corpus, their advantages, and shortcomings as well as proposed several improvements on the existing metrics. The new index better reflects the variety and complexity of a multilingual document. Also, the index can be applied to a sentence and seamlessly extended to a paragraph or an entire document. We have employed two existing code-mixed corpora to suit the requirements of our study. |
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| Published | 2017-07-04 |
| URL | http://arxiv.org/abs/1707.01183v1 |
| http://arxiv.org/pdf/1707.01183v1.pdf | |
| PWC | https://paperswithcode.com/paper/complexity-metric-for-code-mixed-social-media |
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