May 6, 2019

3136 words 15 mins read

Paper Group ANR 164

Uniform {\varepsilon}-Stability of Distributed Nonlinear Filtering over DNAs: Gaussian-Finite HMMs. Interpreting Finite Automata for Sequential Data. Context-aware Sequential Recommendation. Recurrent Neural Radio Anomaly Detection. Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations. …

Uniform {\varepsilon}-Stability of Distributed Nonlinear Filtering over DNAs: Gaussian-Finite HMMs


Title	Uniform {\varepsilon}-Stability of Distributed Nonlinear Filtering over DNAs: Gaussian-Finite HMMs
Authors	Dionysios S. Kalogerias, Athina P. Petropulu
Abstract	In this work, we study stability of distributed filtering of Markov chains with finite state space, partially observed in conditionally Gaussian noise. We consider a nonlinear filtering scheme over a Distributed Network of Agents (DNA), which relies on the distributed evaluation of the likelihood part of the centralized nonlinear filter and is based on a particular specialization of the Alternating Direction Method of Multipliers (ADMM) for fast average consensus. Assuming the same number of consensus steps between any two consecutive noisy measurements for each sensor in the network, we fully characterize a minimal number of such steps, such that the distributed filter remains uniformly stable with a prescribed accuracy level, {\varepsilon} \in (0,1], within a finite operational horizon, T, and across all sensors. Stability is in the sense of the \ell_1-norm between the centralized and distributed versions of the posterior at each sensor, and at each time within T. Roughly speaking, our main result shows that uniform {\varepsilon}-stability of the distributed filtering process depends only loglinearly on T and (roughly) the size of the network, and only logarithmically on 1/{\varepsilon}. If this total loglinear bound is fulfilled, any additional consensus iterations will incur a fully quantified further exponential decay in the consensus error. Our bounds are universal, in the sense that they are independent of the particular structure of the Gaussian Hidden Markov Model (HMM) under consideration.
Tasks
Published	2016-02-16
URL	http://arxiv.org/abs/1602.04912v4
PDF	http://arxiv.org/pdf/1602.04912v4.pdf
PWC	https://paperswithcode.com/paper/uniform-varepsilon-stability-of-distributed
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Interpreting Finite Automata for Sequential Data


Title	Interpreting Finite Automata for Sequential Data
Authors	Christian Albert Hammerschmidt, Sicco Verwer, Qin Lin, Radu State
Abstract	Automaton models are often seen as interpretable models. Interpretability itself is not well defined: it remains unclear what interpretability means without first explicitly specifying objectives or desired attributes. In this paper, we identify the key properties used to interpret automata and propose a modification of a state-merging approach to learn variants of finite state automata. We apply the approach to problems beyond typical grammar inference tasks. Additionally, we cover several use-cases for prediction, classification, and clustering on sequential data in both supervised and unsupervised scenarios to show how the identified key properties are applicable in a wide range of contexts.
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Published	2016-11-21
URL	http://arxiv.org/abs/1611.07100v2
PDF	http://arxiv.org/pdf/1611.07100v2.pdf
PWC	https://paperswithcode.com/paper/interpreting-finite-automata-for-sequential
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Context-aware Sequential Recommendation


Title	Context-aware Sequential Recommendation
Authors	Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li, Liang Wang
Abstract	Since sequential information plays an important role in modeling user behaviors, various sequential recommendation methods have been proposed. Methods based on Markov assumption are widely-used, but independently combine several most recent components. Recently, Recurrent Neural Networks (RNN) based methods have been successfully applied in several sequential modeling tasks. However, for real-world applications, these methods have difficulty in modeling the contextual information, which has been proved to be very important for behavior modeling. In this paper, we propose a novel model, named Context-Aware Recurrent Neural Networks (CA-RNN). Instead of using the constant input matrix and transition matrix in conventional RNN models, CA-RNN employs adaptive context-specific input matrices and adaptive context-specific transition matrices. The adaptive context-specific input matrices capture external situations where user behaviors happen, such as time, location, weather and so on. And the adaptive context-specific transition matrices capture how lengths of time intervals between adjacent behaviors in historical sequences affect the transition of global sequential features. Experimental results show that the proposed CA-RNN model yields significant improvements over state-of-the-art sequential recommendation methods and context-aware recommendation methods on two public datasets, i.e., the Taobao dataset and the Movielens-1M dataset.
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Published	2016-09-19
URL	http://arxiv.org/abs/1609.05787v1
PDF	http://arxiv.org/pdf/1609.05787v1.pdf
PWC	https://paperswithcode.com/paper/context-aware-sequential-recommendation
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Recurrent Neural Radio Anomaly Detection


Title	Recurrent Neural Radio Anomaly Detection
Authors	Timothy J O’Shea, T. Charles Clancy, Robert W. McGwier
Abstract	We introduce a powerful recurrent neural network based method for novelty detection to the application of detecting radio anomalies. This approach holds promise in significantly increasing the ability of naive anomaly detection to detect small anomalies in highly complex complexity multi-user radio bands. We demonstrate the efficacy of this approach on a number of common real over the air radio communications bands of interest and quantify detection performance in terms of probability of detection an false alarm rates across a range of interference to band power ratios and compare to baseline methods.
Tasks	Anomaly Detection
Published	2016-11-01
URL	http://arxiv.org/abs/1611.00301v1
PDF	http://arxiv.org/pdf/1611.00301v1.pdf
PWC	https://paperswithcode.com/paper/recurrent-neural-radio-anomaly-detection
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Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations


Title	Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations
Authors	Hao Wu, Feliks Nüske, Fabian Paul, Stefan Klus, Peter Koltai, Frank Noé
Abstract	Markov state models (MSMs) and Master equation models are popular approaches to approximate molecular kinetics, equilibria, metastable states, and reaction coordinates in terms of a state space discretization usually obtained by clustering. Recently, a powerful generalization of MSMs has been introduced, the variational approach (VA) of molecular kinetics and its special case the time-lagged independent component analysis (TICA), which allow us to approximate slow collective variables and molecular kinetics by linear combinations of smooth basis functions or order parameters. While it is known how to estimate MSMs from trajectories whose starting points are not sampled from an equilibrium ensemble, this has not yet been the case for TICA and the VA. Previous estimates from short trajectories, have been strongly biased and thus not variationally optimal. Here, we employ Koopman operator theory and ideas from dynamic mode decomposition (DMD) to extend the VA and TICA to non-equilibrium data. The main insight is that the VA and TICA provide a coefficient matrix that we call Koopman model, as it approximates the underlying dynamical (Koopman) operator in conjunction with the basis set used. This Koopman model can be used to compute a stationary vector to reweight the data to equilibrium. From such a Koopman-reweighted sample, equilibrium expectation values and variationally optimal reversible Koopman models can be constructed even with short simulations. The Koopman model can be used to propagate densities, and its eigenvalue decomposition provide estimates of relaxation timescales and slow collective variables for dimension reduction. Koopman models are generalizations of Markov state models, TICA and the linear VA and allow molecular kinetics to be described without a cluster discretization.
Tasks	Dimensionality Reduction
Published	2016-10-20
URL	http://arxiv.org/abs/1610.06773v2
PDF	http://arxiv.org/pdf/1610.06773v2.pdf
PWC	https://paperswithcode.com/paper/variational-koopman-models-slow-collective
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Title	Multi-modal Fusion for Diabetes Mellitus and Impaired Glucose Regulation Detection
Authors	Jinxing Li, David Zhang, Yongcheng Li, Jian Wu
Abstract	Effective and accurate diagnosis of Diabetes Mellitus (DM), as well as its early stage Impaired Glucose Regulation (IGR), has attracted much attention recently. Traditional Chinese Medicine (TCM) [3], [5] etc. has proved that tongue, face and sublingual diagnosis as a noninvasive method is a reasonable way for disease detection. However, most previous works only focus on a single modality (tongue, face or sublingual) for diagnosis, although different modalities may provide complementary information for the diagnosis of DM and IGR. In this paper, we propose a novel multi-modal classification method to discriminate between DM (or IGR) and healthy controls. Specially, the tongue, facial and sublingual images are first collected by using a non-invasive capture device. The color, texture and geometry features of these three types of images are then extracted, respectively. Finally, our so-called multi-modal similar and specific learning (MMSSL) approach is proposed to combine features of tongue, face and sublingual, which not only exploits the correlation but also extracts individual components among them. Experimental results on a dataset consisting of 192 Healthy, 198 DM and 114 IGR samples (all samples were obtained from Guangdong Provincial Hospital of Traditional Chinese Medicine) substantiate the effectiveness and superiority of our proposed method for the diagnosis of DM and IGR, compared to the case of using a single modality.
Tasks
Published	2016-04-12
URL	http://arxiv.org/abs/1604.03443v1
PDF	http://arxiv.org/pdf/1604.03443v1.pdf
PWC	https://paperswithcode.com/paper/multi-modal-fusion-for-diabetes-mellitus-and
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Distributed Machine Learning in Materials that Couple Sensing, Actuation, Computation and Communication


Title	Distributed Machine Learning in Materials that Couple Sensing, Actuation, Computation and Communication
Authors	Dana Hughes, Nikolaus Correll
Abstract	This paper reviews machine learning applications and approaches to detection, classification and control of intelligent materials and structures with embedded distributed computation elements. The purpose of this survey is to identify desired tasks to be performed in each type of material or structure (e.g., damage detection in composites), identify and compare common approaches to learning such tasks, and investigate models and training paradigms used. Machine learning approaches and common temporal features used in the domains of structural health monitoring, morphable aircraft, wearable computing and robotic skins are explored. As the ultimate goal of this research is to incorporate the approaches described in this survey into a robotic material paradigm, the potential for adapting the computational models used in these applications, and corresponding training algorithms, to an amorphous network of computing nodes is considered. Distributed versions of support vector machines, graphical models and mixture models developed in the field of wireless sensor networks are reviewed. Potential areas of investigation, including possible architectures for incorporating machine learning into robotic nodes, training approaches, and the possibility of using deep learning approaches for automatic feature extraction, are discussed.
Tasks
Published	2016-06-11
URL	http://arxiv.org/abs/1606.03508v1
PDF	http://arxiv.org/pdf/1606.03508v1.pdf
PWC	https://paperswithcode.com/paper/distributed-machine-learning-in-materials
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Learning Possibilistic Logic Theories from Default Rules


Title	Learning Possibilistic Logic Theories from Default Rules
Authors	Ondrej Kuzelka, Jesse Davis, Steven Schockaert
Abstract	We introduce a setting for learning possibilistic logic theories from defaults of the form “if alpha then typically beta”. We first analyse this problem from the point of view of machine learning theory, determining the VC dimension of possibilistic stratifications as well as the complexity of the associated learning problems, after which we present a heuristic learning algorithm that can easily scale to thousands of defaults. An important property of our approach is that it is inherently able to handle noisy and conflicting sets of defaults. Among others, this allows us to learn possibilistic logic theories from crowdsourced data and to approximate propositional Markov logic networks using heuristic MAP solvers. We present experimental results that demonstrate the effectiveness of this approach.
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Published	2016-04-18
URL	http://arxiv.org/abs/1604.05273v1
PDF	http://arxiv.org/pdf/1604.05273v1.pdf
PWC	https://paperswithcode.com/paper/learning-possibilistic-logic-theories-from
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Statistical learning for wind power : a modeling and stability study towards forecasting


Title	Statistical learning for wind power : a modeling and stability study towards forecasting
Authors	Aurélie Fischer, Lucie Montuelle, Mathilde Mougeot, Dominique Picard
Abstract	We focus on wind power modeling using machine learning techniques. We show on real data provided by the wind energy company Ma{"i}a Eolis, that parametric models, even following closely the physical equation relating wind production to wind speed are outperformed by intelligent learning algorithms. In particular, the CART-Bagging algorithm gives very stable and promising results. Besides, as a step towards forecast, we quantify the impact of using deteriorated wind measures on the performances. We show also on this application that the default methodology to select a subset of predictors provided in the standard random forest package can be refined, especially when there exists among the predictors one variable which has a major impact.
Tasks
Published	2016-10-04
URL	http://arxiv.org/abs/1610.01000v2
PDF	http://arxiv.org/pdf/1610.01000v2.pdf
PWC	https://paperswithcode.com/paper/statistical-learning-for-wind-power-a
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Active learning with version spaces for object detection


Title	Active learning with version spaces for object detection
Authors	Soumya Roy, Vinay P. Namboodiri, Arijit Biswas
Abstract	Given an image, we would like to learn to detect objects belonging to particular object categories. Common object detection methods train on large annotated datasets which are annotated in terms of bounding boxes that contain the object of interest. Previous works on object detection model the problem as a structured regression problem which ranks the correct bounding boxes more than the background ones. In this paper we develop algorithms which actively obtain annotations from human annotators for a small set of images, instead of all images, thereby reducing the annotation effort. Towards this goal, we make the following contributions: 1. We develop a principled version space based active learning method that solves for object detection as a structured prediction problem in a weakly supervised setting 2. We also propose two variants of the margin sampling strategy 3. We analyse the results on standard object detection benchmarks that show that with only 20% of the data we can obtain more than 95% of the localization accuracy of full supervision. Our methods outperform random sampling and the classical uncertainty-based active learning algorithms like entropy
Tasks	Active Learning, Object Detection, Structured Prediction
Published	2016-11-22
URL	http://arxiv.org/abs/1611.07285v2
PDF	http://arxiv.org/pdf/1611.07285v2.pdf
PWC	https://paperswithcode.com/paper/active-learning-with-version-spaces-for
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Extracting Higher-Order Goals from the Mizar Mathematical Library


Title	Extracting Higher-Order Goals from the Mizar Mathematical Library
Authors	Chad Brown, Josef Urban
Abstract	Certain constructs allowed in Mizar articles cannot be represented in first-order logic but can be represented in higher-order logic. We describe a way to obtain higher-order theorem proving problems from Mizar articles that make use of these constructs. In particular, higher-order logic is used to represent schemes, a global choice construct and set level binders. The higher-order automated theorem provers Satallax and LEO-II have been run on collections of these problems and the results are discussed.
Tasks	Automated Theorem Proving
Published	2016-05-23
URL	http://arxiv.org/abs/1605.06996v1
PDF	http://arxiv.org/pdf/1605.06996v1.pdf
PWC	https://paperswithcode.com/paper/extracting-higher-order-goals-from-the-mizar
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EXTRACT: Strong Examples from Weakly-Labeled Sensor Data


Title	EXTRACT: Strong Examples from Weakly-Labeled Sensor Data
Authors	Davis W. Blalock, John V. Guttag
Abstract	Thanks to the rise of wearable and connected devices, sensor-generated time series comprise a large and growing fraction of the world’s data. Unfortunately, extracting value from this data can be challenging, since sensors report low-level signals (e.g., acceleration), not the high-level events that are typically of interest (e.g., gestures). We introduce a technique to bridge this gap by automatically extracting examples of real-world events in low-level data, given only a rough estimate of when these events have taken place. By identifying sets of features that repeat in the same temporal arrangement, we isolate examples of such diverse events as human actions, power consumption patterns, and spoken words with up to 96% precision and recall. Our method is fast enough to run in real time and assumes only minimal knowledge of which variables are relevant or the lengths of events. Our evaluation uses numerous publicly available datasets and over 1 million samples of manually labeled sensor data.
Tasks	Time Series
Published	2016-09-29
URL	http://arxiv.org/abs/1609.09196v1
PDF	http://arxiv.org/pdf/1609.09196v1.pdf
PWC	https://paperswithcode.com/paper/extract-strong-examples-from-weakly-labeled
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Multilevel Thresholding Segmentation of T2 weighted Brain MRI images using Convergent Heterogeneous Particle Swarm Optimization


Title	Multilevel Thresholding Segmentation of T2 weighted Brain MRI images using Convergent Heterogeneous Particle Swarm Optimization
Authors	Mohammad Hamed Mozaffari, Won-Sook Lee
Abstract	This paper proposes a new image thresholding segmentation approach using the heuristic method, Convergent Heterogeneous Particle Swarm Optimization algorithm. The proposed algorithm incorporates a new strategy of searching the problem space by dividing the swarm into subswarms. Each subswarm particles search for better solution separately lead to better exploitation while they cooperate with each other to find the best global position. The consequence of the aforementioned cooperation is better exploration, convergence and it able the algorithm to jump from local optimal solution to the better spots. A practical application of this method is demonstrated for the problem of medical image thresholding segmentation. We considered two classical thresholding techniques of Otsu and Kapur separately as the objective function for the optimization method and applied on a set of brain MR images. Comparative experimental results reveal that the proposed method outperforms another state of the art method from the literature in terms of accuracy, computation time and stable results.
Tasks
Published	2016-05-16
URL	http://arxiv.org/abs/1605.04806v1
PDF	http://arxiv.org/pdf/1605.04806v1.pdf
PWC	https://paperswithcode.com/paper/multilevel-thresholding-segmentation-of-t2
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Analysis of Algorithms and Partial Algorithms


Title	Analysis of Algorithms and Partial Algorithms
Authors	Andrew MacFie
Abstract	We present an alternative methodology for the analysis of algorithms, based on the concept of expected discounted reward. This methodology naturally handles algorithms that do not always terminate, so it can (theoretically) be used with partial algorithms for undecidable problems, such as those found in artificial general intelligence (AGI) and automated theorem proving. We mention an approach to self-improving AGI enabled by this methodology. Aug 2017 addendum: This article was originally written with multiple audiences in mind. It is really best put in the following terms. Goertzel, Hutter, Legg, and others have developed a definition of an intelligence score for a general abstract agent: expected lifetime reward in a random environment. AIXI is generally the optimal agent according to this score, but there may be reasons to analyze other agents and compare score values. If we want to use this definition of intelligence in practice, perhaps we can start by analyzing some simple agents. Common algorithms can be thought of as simple agents (environment is input, reward is based on running time) so we take the goal of applying the agent intelligence score to algorithms. That is, we want to find, what are the IQ scores of algorithms? We can do some very simple analysis, but the real answer is that even for simple algorithms, the intelligence score is too difficult to work with in practice.
Tasks	Automated Theorem Proving
Published	2016-01-13
URL	http://arxiv.org/abs/1601.03411v5
PDF	http://arxiv.org/pdf/1601.03411v5.pdf
PWC	https://paperswithcode.com/paper/analysis-of-algorithms-and-partial-algorithms
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Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback


Title	Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback
Authors	Zheng Wen, Branislav Kveton, Michal Valko, Sharan Vaswani
Abstract	We study the online influence maximization problem in social networks under the independent cascade model. Specifically, we aim to learn the set of “best influencers” in a social network online while repeatedly interacting with it. We address the challenges of (i) combinatorial action space, since the number of feasible influencer sets grows exponentially with the maximum number of influencers, and (ii) limited feedback, since only the influenced portion of the network is observed. Under a stochastic semi-bandit feedback, we propose and analyze IMLinUCB, a computationally efficient UCB-based algorithm. Our bounds on the cumulative regret are polynomial in all quantities of interest, achieve near-optimal dependence on the number of interactions and reflect the topology of the network and the activation probabilities of its edges, thereby giving insights on the problem complexity. To the best of our knowledge, these are the first such results. Our experiments show that in several representative graph topologies, the regret of IMLinUCB scales as suggested by our upper bounds. IMLinUCB permits linear generalization and thus is both statistically and computationally suitable for large-scale problems. Our experiments also show that IMLinUCB with linear generalization can lead to low regret in real-world online influence maximization.
Tasks
Published	2016-05-21
URL	http://arxiv.org/abs/1605.06593v3
PDF	http://arxiv.org/pdf/1605.06593v3.pdf
PWC	https://paperswithcode.com/paper/online-influence-maximization-under
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