January 27, 2020

2823 words 14 mins read

Paper Group ANR 1324

$DC^2$: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering. Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles. Crime Analysis using Open Source Information. Augmenting Non-Collaborative Dialog Systems with Explicit Semantic and Strategic Dialog History. Challenges in Building Inte …

$DC^2$: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering


Title	$DC^2$: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering
Authors	Ke Alexander Wang, Xinran Bian, Pan Liu, Donghui Yan
Abstract	Divide-and-conquer is a general strategy to deal with large scale problems. It is typically applied to generate ensemble instances, which potentially limits the problem size it can handle. Additionally, the data are often divided by random sampling which may be suboptimal. To address these concerns, we propose the $DC^2$ algorithm. Instead of ensemble instances, we produce structure-preserving signature pieces to be assembled and conquered. $DC^2$ achieves the efficiency of sampling-based large scale kernel methods while enabling parallel multicore or clustered computation. The data partition and subsequent compression are unified by recursive random projections. Empirically dividing the data by random projections induces smaller mean squared approximation errors than conventional random sampling. The power of $DC^2$ is demonstrated by our clustering algorithm $rpfCluster^+$, which is as accurate as some fastest approximate spectral clustering algorithms while maintaining a running time close to that of K-means clustering. Analysis on $DC^2$ when applied to spectral clustering shows that the loss in clustering accuracy due to data division and reduction is upper bounded by the data approximation error which would vanish with recursive random projections. Due to its easy implementation and flexibility, we expect $DC^2$ to be applicable to general large scale learning problems.
Tasks
Published	2019-11-16
URL	https://arxiv.org/abs/1911.06944v1
PDF	https://arxiv.org/pdf/1911.06944v1.pdf
PWC	https://paperswithcode.com/paper/dc2-a-divide-and-conquer-algorithm-for-large
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Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles


Title	Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles
Authors	Pin Wang, Hanhan Li, Ching-Yao Chan
Abstract	Reinforcement Learning algorithms have recently been proposed to learn time-sequential control policies in the field of autonomous driving. Direct applications of Reinforcement Learning algorithms with discrete action space will yield unsatisfactory results at the operational level of driving where continuous control actions are actually required. In addition, the design of neural networks often fails to incorporate the domain knowledge of the targeting problem such as the classical control theories in our case. In this paper, we propose a hybrid model by combining Q-learning and classic PID (Proportion Integration Differentiation) controller for handling continuous vehicle control problems under dynamic driving environment. Particularly, instead of using a big neural network as Q-function approximation, we design a Quadratic Q-function over actions with multiple simple neural networks for finding optimal values within a continuous space. We also build an action network based on the domain knowledge of the control mechanism of a PID controller to guide the agent to explore optimal actions more efficiently.We test our proposed approach in simulation under two common but challenging driving situations, the lane change scenario and ramp merge scenario. Results show that the autonomous vehicle agent can successfully learn a smooth and efficient driving behavior in both situations.
Tasks	Autonomous Driving, Autonomous Vehicles, Continuous Control, Q-Learning
Published	2019-11-29
URL	https://arxiv.org/abs/1912.00074v1
PDF	https://arxiv.org/pdf/1912.00074v1.pdf
PWC	https://paperswithcode.com/paper/quadratic-q-network-for-learning-continuous
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Crime Analysis using Open Source Information


Title	Crime Analysis using Open Source Information
Authors	Sarwat Nizamani, Nasrullah Memon, Azhar Ali Shah, Sehrish Nizamani, Saad Nizamani, Imdad Ali Ismaili
Abstract	In this paper, we present a method of crime analysis from open source information. We employed un-supervised methods of data mining to explore the facts regarding the crimes of an area of interest. The analysis is based on well known clustering and association techniques. The results show that the proposed method of crime analysis is efficient and gives a broad picture of the crimes of an area to analyst without much effort. The analysis is evaluated using manual approach, which reveals that the results produced by the proposed approach are comparable to the manual analysis, while a great amount of time is saved.
Tasks
Published	2019-02-15
URL	http://arxiv.org/abs/1902.05684v1
PDF	http://arxiv.org/pdf/1902.05684v1.pdf
PWC	https://paperswithcode.com/paper/crime-analysis-using-open-source-information
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Augmenting Non-Collaborative Dialog Systems with Explicit Semantic and Strategic Dialog History


Title	Augmenting Non-Collaborative Dialog Systems with Explicit Semantic and Strategic Dialog History
Authors	Yiheng Zhou, Yulia Tsvetkov, Alan W Black, Zhou Yu
Abstract	We study non-collaborative dialogs, where two agents have a conflict of interest but must strategically communicate to reach an agreement (e.g., negotiation). This setting poses new challenges for modeling dialog history because the dialog’s outcome relies not only on the semantic intent, but also on tactics that convey the intent. We propose to model both semantic and tactic history using finite state transducers (FSTs). Unlike RNN, FSTs can explicitly represent dialog history through all the states traversed, facilitating interpretability of dialog structure. We train FSTs on a set of strategies and tactics used in negotiation dialogs. The trained FSTs show plausible tactic structure and can be generalized to other non-collaborative domains (e.g., persuasion). We evaluate the FSTs by incorporating them in an automated negotiating system that attempts to sell products and a persuasion system that persuades people to donate to a charity. Experiments show that explicitly modeling both semantic and tactic history is an effective way to improve both dialog policy planning and generation performance.
Tasks
Published	2019-09-30
URL	https://arxiv.org/abs/1909.13425v1
PDF	https://arxiv.org/pdf/1909.13425v1.pdf
PWC	https://paperswithcode.com/paper/augmenting-non-collaborative-dialog-systems
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Challenges in Building Intelligent Open-domain Dialog Systems


Title	Challenges in Building Intelligent Open-domain Dialog Systems
Authors	Minlie Huang, Xiaoyan Zhu, Jianfeng Gao
Abstract	There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational AI. Unlike traditional task-oriented bots, an open-domain dialog system aims to establish long-term connections with users by satisfying the human need for communication, affection, and social belonging. This paper reviews the recent works on neural approaches that are devoted to addressing three challenges in developing such systems: semantics, consistency, and interactiveness. Semantics requires a dialog system to not only understand the content of the dialog but also identify user’s social needs during the conversation. Consistency requires the system to demonstrate a consistent personality to win users trust and gain their long-term confidence. Interactiveness refers to the system’s ability to generate interpersonal responses to achieve particular social goals such as entertainment, conforming, and task completion. The works we select to present here is based on our unique views and are by no means complete. Nevertheless, we hope that the discussion will inspire new research in developing more intelligent dialog systems.
Tasks
Published	2019-05-13
URL	https://arxiv.org/abs/1905.05709v3
PDF	https://arxiv.org/pdf/1905.05709v3.pdf
PWC	https://paperswithcode.com/paper/challenges-in-building-intelligent-open
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Curriculum Learning for Domain Adaptation in Neural Machine Translation


Title	Curriculum Learning for Domain Adaptation in Neural Machine Translation
Authors	Xuan Zhang, Pamela Shapiro, Gaurav Kumar, Paul McNamee, Marine Carpuat, Kevin Duh
Abstract	We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular schedule. This approach is simple to implement on top of any neural framework or architecture, and consistently outperforms both unadapted and adapted baselines in experiments with two distinct domains and two language pairs.
Tasks	Domain Adaptation, Machine Translation
Published	2019-05-14
URL	https://arxiv.org/abs/1905.05816v1
PDF	https://arxiv.org/pdf/1905.05816v1.pdf
PWC	https://paperswithcode.com/paper/curriculum-learning-for-domain-adaptation-in
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Privacy-preserving and yet Robust Collaborative Filtering Recommender as a Service


Title	Privacy-preserving and yet Robust Collaborative Filtering Recommender as a Service
Authors	Qiang Tang
Abstract	Collaborative filtering recommenders provide effective personalization services at the cost of sacrificing the privacy of their end users. Due to the increasing concerns from the society and stricter privacy regulations, it is an urgent research challenge to design privacy-preserving and yet robust recommenders which offer recommendation services to privacy-aware users. Our analysis shows that existing solutions fall short in several aspects, including lacking attention to the precise output to end users and ignoring the correlated robustness issues. In this paper, we provide a general system structure for latent factor based collaborative filtering recommenders by formulating them into model training and prediction computing stages, and also describe a new security model. Aiming at pragmatic solutions, we first show how to construct privacy-preserving and yet robust model training stage based on existing solutions. Then, we propose two cryptographic protocols to realize a privacy-preserving prediction computing stage, depending on whether or not an extra proxy is involved. Different from standard Top-k recommendations, we alternatively let the end user retrieve the unrated items whose predictions are above a threshold, as a result of our privacy by design strategy. Experimental results show that our new protocols are quite efficient.
Tasks
Published	2019-10-09
URL	https://arxiv.org/abs/1910.03846v1
PDF	https://arxiv.org/pdf/1910.03846v1.pdf
PWC	https://paperswithcode.com/paper/privacy-preserving-and-yet-robust
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A persistent homology approach to heart rate variability analysis with an application to sleep-wake classification


Title	A persistent homology approach to heart rate variability analysis with an application to sleep-wake classification
Authors	Yu-Min Chung, Chuan-Shen Hu, Yu-Lun Lo, Hau-Tieng Wu
Abstract	Persistent homology (PH) is a recently developed theory in the field of algebraic topology. It is an effective and robust tool to study shapes of datasets and has been widely applied. We demonstrate a general pipeline to apply PH to study time series; particularly the heart rate variability (HRV). First, we study the shapes of time series in two different ways – sub-level set and Taken’s lag map. Second, we propose a systematic approach to summarize/vectorize persistence diagrams, a companion tool of PH. To demonstrate our proposed method, we apply these tools to the HRV analysis and the sleep-wake, REM-NREM (rapid eyeball movement and non rapid eyeball movement) and sleep-REM-NREM classification problems. The proposed algorithm is evaluated on three different datasets via the cross-database validation scheme. The performance of our approach is comparable with the state-of-the-art algorithms, and are consistent throughout these different datasets.
Tasks	Heart Rate Variability, Time Series
Published	2019-08-09
URL	https://arxiv.org/abs/1908.06856v1
PDF	https://arxiv.org/pdf/1908.06856v1.pdf
PWC	https://paperswithcode.com/paper/a-persistent-homology-approach-to-heart-rate
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Sanity Checks for Saliency Metrics


Title	Sanity Checks for Saliency Metrics
Authors	Richard Tomsett, Dan Harborne, Supriyo Chakraborty, Prudhvi Gurram, Alun Preece
Abstract	Saliency maps are a popular approach to creating post-hoc explanations of image classifier outputs. These methods produce estimates of the relevance of each pixel to the classification output score, which can be displayed as a saliency map that highlights important pixels. Despite a proliferation of such methods, little effort has been made to quantify how good these saliency maps are at capturing the true relevance of the pixels to the classifier output (i.e. their “fidelity”). We therefore investigate existing metrics for evaluating the fidelity of saliency methods (i.e. saliency metrics). We find that there is little consistency in the literature in how such metrics are calculated, and show that such inconsistencies can have a significant effect on the measured fidelity. Further, we apply measures of reliability developed in the psychometric testing literature to assess the consistency of saliency metrics when applied to individual saliency maps. Our results show that saliency metrics can be statistically unreliable and inconsistent, indicating that comparative rankings between saliency methods generated using such metrics can be untrustworthy.
Tasks
Published	2019-11-29
URL	https://arxiv.org/abs/1912.01451v1
PDF	https://arxiv.org/pdf/1912.01451v1.pdf
PWC	https://paperswithcode.com/paper/sanity-checks-for-saliency-metrics
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On the k-synchronizability of systems


Title	On the k-synchronizability of systems
Authors	Cinzia Di Giusto, Cinzia Giusto, Laetitia Laversa, Etienne Lozes
Abstract	In this paper, we work on the notion of k-synchronizability: a system is k-synchronizable if any of its executions, up to reordering causally independent actions, can be divided into a succession of k-bounded interaction phases. We show two results (both for mailbox and peer-to-peer automata): first, the reachability problem is decidable for k-synchronizable systems; second, the membership problem (whether a given system is k-synchronizable) is decidable as well. Our proofs fix several important issues in previous attempts to prove these two results for mailbox automata.
Tasks
Published	2019-09-04
URL	https://arxiv.org/abs/1909.01627v2
PDF	https://arxiv.org/pdf/1909.01627v2.pdf
PWC	https://paperswithcode.com/paper/on-the-k-synchronizability-for-mailbox
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Hypergraph Convolution and Hypergraph Attention


Title	Hypergraph Convolution and Hypergraph Attention
Authors	Song Bai, Feihu Zhang, Philip H. S. Torr
Abstract	Recently, graph neural networks have attracted great attention and achieved prominent performance in various research fields. Most of those algorithms have assumed pairwise relationships of objects of interest. However, in many real applications, the relationships between objects are in higher-order, beyond a pairwise formulation. To efficiently learn deep embeddings on the high-order graph-structured data, we introduce two end-to-end trainable operators to the family of graph neural networks, i.e., hypergraph convolution and hypergraph attention. Whilst hypergraph convolution defines the basic formulation of performing convolution on a hypergraph, hypergraph attention further enhances the capacity of representation learning by leveraging an attention module. With the two operators, a graph neural network is readily extended to a more flexible model and applied to diverse applications where non-pairwise relationships are observed. Extensive experimental results with semi-supervised node classification demonstrate the effectiveness of hypergraph convolution and hypergraph attention.
Tasks	Node Classification, Representation Learning
Published	2019-01-23
URL	http://arxiv.org/abs/1901.08150v1
PDF	http://arxiv.org/pdf/1901.08150v1.pdf
PWC	https://paperswithcode.com/paper/hypergraph-convolution-and-hypergraph
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High-Confidence Policy Optimization: Reshaping Ambiguity Sets in Robust MDPs


Title	High-Confidence Policy Optimization: Reshaping Ambiguity Sets in Robust MDPs
Authors	Bahram Behzadian, Reazul Hasan Russel, Marek Petrik
Abstract	Robust MDPs are a promising framework for computing robust policies in reinforcement learning. Ambiguity sets, which represent the plausible errors in transition probabilities, determine the trade-off between robustness and average-case performance. The standard practice of defining ambiguity sets using the $L_1$ norm leads, unfortunately, to loose and impractical guarantees. This paper describes new methods for optimizing the shape of ambiguity sets beyond the $L_1$ norm. We derive new high-confidence sampling bounds for weighted $L_1$ and weighted $L_\infty$ ambiguity sets and describe how to compute near-optimal weights from rough value function estimates. Experimental results on a diverse set of benchmarks show that optimized ambiguity sets provide significantly tighter robustness guarantees.
Tasks
Published	2019-10-23
URL	https://arxiv.org/abs/1910.10786v2
PDF	https://arxiv.org/pdf/1910.10786v2.pdf
PWC	https://paperswithcode.com/paper/high-confidence-policy-optimization-reshaping
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How much real data do we actually need: Analyzing object detection performance using synthetic and real data


Title	How much real data do we actually need: Analyzing object detection performance using synthetic and real data
Authors	Farzan Erlik Nowruzi, Prince Kapoor, Dhanvin Kolhatkar, Fahed Al Hassanat, Robert Laganiere, Julien Rebut
Abstract	In recent years, deep learning models have resulted in a huge amount of progress in various areas, including computer vision. By nature, the supervised training of deep models requires a large amount of data to be available. This ideal case is usually not tractable as the data annotation is a tremendously exhausting and costly task to perform. An alternative is to use synthetic data. In this paper, we take a comprehensive look into the effects of replacing real data with synthetic data. We further analyze the effects of having a limited amount of real data. We use multiple synthetic and real datasets along with a simulation tool to create large amounts of cheaply annotated synthetic data. We analyze the domain similarity of each of these datasets. We provide insights about designing a methodological procedure for training deep networks using these datasets.
Tasks	Object Detection
Published	2019-07-16
URL	https://arxiv.org/abs/1907.07061v1
PDF	https://arxiv.org/pdf/1907.07061v1.pdf
PWC	https://paperswithcode.com/paper/how-much-real-data-do-we-actually-need
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Hidden Markov Models for sepsis detection in preterm infants


Title	Hidden Markov Models for sepsis detection in preterm infants
Authors	Antoine Honore, Dong Liu, David Forsberg, Karen Coste, Eric Herlenius, Saikat Chatterjee, Mikael Skoglund
Abstract	We explore the use of traditional and contemporary hidden Markov models (HMMs) for sequential physiological data analysis and sepsis prediction in preterm infants. We investigate the use of classical Gaussian mixture model based HMM, and a recently proposed neural network based HMM. To improve the neural network based HMM, we propose a discriminative training approach. Experimental results show the potential of HMMs over logistic regression, support vector machine and extreme learning machine.
Tasks
Published	2019-10-30
URL	https://arxiv.org/abs/1910.13904v1
PDF	https://arxiv.org/pdf/1910.13904v1.pdf
PWC	https://paperswithcode.com/paper/hidden-markov-models-for-sepsis-detection-in
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Layered SGD: A Decentralized and Synchronous SGD Algorithm for Scalable Deep Neural Network Training


Title	Layered SGD: A Decentralized and Synchronous SGD Algorithm for Scalable Deep Neural Network Training
Authors	Kwangmin Yu, Thomas Flynn, Shinjae Yoo, Nicholas D’Imperio
Abstract	Stochastic Gradient Descent (SGD) is the most popular algorithm for training deep neural networks (DNNs). As larger networks and datasets cause longer training times, training on distributed systems is common and distributed SGD variants, mainly asynchronous and synchronous SGD, are widely used. Asynchronous SGD is communication efficient but suffers from accuracy degradation due to delayed parameter updating. Synchronous SGD becomes communication intensive when the number of nodes increases regardless of its advantage. To address these issues, we introduce Layered SGD (LSGD), a new decentralized synchronous SGD algorithm. LSGD partitions computing resources into subgroups that each contain a communication layer (communicator) and a computation layer (worker). Each subgroup has centralized communication for parameter updates while communication between subgroups is handled by communicators. As a result, communication time is overlapped with I/O latency of workers. The efficiency of the algorithm is tested by training a deep network on the ImageNet classification task.
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Published	2019-06-13
URL	https://arxiv.org/abs/1906.05936v1
PDF	https://arxiv.org/pdf/1906.05936v1.pdf
PWC	https://paperswithcode.com/paper/layered-sgd-a-decentralized-and-synchronous
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