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35
A Constraint Generation Approach to Learning Stable Linear Dynamical Systems
"... Stability is a desirable characteristic for linear dynamical systems, but it is often ignored by algorithms that learn these systems from data. We propose a novel method for learning stable linear dynamical systems: we formulate an approximation of the problem as a convex program, start with a solut ..."
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Cited by 31 (8 self)
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Stability is a desirable characteristic for linear dynamical systems, but it is often ignored by algorithms that learn these systems from data. We propose a novel method for learning stable linear dynamical systems: we formulate an approximation of the problem as a convex program, start with a solution to a relaxed version of the program, and incrementally add constraints to improve stability. Rather than continuing to generate constraints until we reach a feasible solution, we test stability at each step; because the convex program is only an approximation of the desired problem, this early stopping rule can yield a higherquality solution. We apply our algorithm to the task of learning dynamic textures from image sequences as well as to modeling biosurveillance drugsales data. The constraint generation approach leads to noticeable improvement in the quality of simulated sequences. We compare our method to those of Lacy and Bernstein [1, 2], with positive results in terms of accuracy, quality of simulated sequences, and efficiency.
Highlevel goal recognition in a wireless LAN
 In: Proceedings of the 19th AAAI Conference on Artificial Intelligence
, 2004
"... Plan recognition has traditionally been developed for logically encoded application domains with a focus on logical reasoning. In this paper, we present an integrated planrecognition model that combines lowlevel sensory readings with highlevel goal inference. A twolevel architecture is proposed t ..."
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Cited by 27 (15 self)
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Plan recognition has traditionally been developed for logically encoded application domains with a focus on logical reasoning. In this paper, we present an integrated planrecognition model that combines lowlevel sensory readings with highlevel goal inference. A twolevel architecture is proposed to infer a user’s goals in a complex indoor environment using an RFbased wireless network. The novelty of our work derives from our ability to infer a user’s goals from sequences of signal trajectory, and the ability for us to make a tradeoff between model accuracy and inference efficiency. The model relies on a dynamic Bayesian network to infer a user’s actions from raw signals, and an Ngram model to infer the users ’ goals from actions. We present a method for constructing the model from the past data and demonstrate the effectiveness of our proposed solution through empirical studies using some real data that we have collected.
Hidden process models
 In International Conference of Machine Learning ICML
, 2006
"... We introduce Hidden Process Models (HPMs), a class of probabilistic models for multivariate time series data. The design of HPMs has been motivated by the challenges of modeling hidden cognitive processes in the brain, given functional Magnetic Resonance Imaging (fMRI) data. fMRI data is sparse, hig ..."
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Cited by 10 (4 self)
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We introduce Hidden Process Models (HPMs), a class of probabilistic models for multivariate time series data. The design of HPMs has been motivated by the challenges of modeling hidden cognitive processes in the brain, given functional Magnetic Resonance Imaging (fMRI) data. fMRI data is sparse, highdimensional, nonMarkovian, and often involves prior knowledge of the form “hidden event A occurs n times within the interval [t,t ′]. ” HPMs provide a generalization of the widely used General Linear Model approaches to fMRI analysis, and HPMs can also be viewed as a subclass of Dynamic Bayes Networks.
Probabilistic Parameter Selection for Learning Scene Structure from Video
"... We present an online learning approach for robustly combining unreliable observations from a pedestrian detector to estimate the rough 3D scene geometry from video sequences of a static camera. Our approach is based on an entropy modelling framework, which allows to simultaneously adapt the detector ..."
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Cited by 6 (1 self)
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We present an online learning approach for robustly combining unreliable observations from a pedestrian detector to estimate the rough 3D scene geometry from video sequences of a static camera. Our approach is based on an entropy modelling framework, which allows to simultaneously adapt the detector parameters, such that the expected information gain about the scene structure is maximised. As a result, our approach automatically restricts the detector scale range for each image region as the estimation results become more confident, thus improving detector runtime and limiting false positives. 1
Activity Recognition using Dynamic Bayesian Networks with Automatic State Selection
 IEEE Workshop on Motion and Video Computing(WMVC
, 2007
"... Applying advanced video technology to understand activity and intent is becoming increasingly important for intelligent video surveillance. We present a general model of a dlevel dynamic Bayesian network to perform complex event recognition. The levels of the network are constrained to enforce stat ..."
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Cited by 6 (2 self)
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Applying advanced video technology to understand activity and intent is becoming increasingly important for intelligent video surveillance. We present a general model of a dlevel dynamic Bayesian network to perform complex event recognition. The levels of the network are constrained to enforce state hierarchy while the d th level models the duration of simplest event. Moreover, in this paper we propose to use the deterministic annealing clustering method to automatically discover the states for the observable levels. We used real world data sets to show the effectiveness of our proposed method. 1.
Online Probability Density Estimation of Nonstationary Random Signal using Dynamic Bayesian Networks
"... Abstract: We present two estimators for discrete nonGaussian and nonstationary probability density estimation based on a dynamic Bayesian network (DBN). The first estimator is for offline computation and consists of a DBN whose transition distribution is represented in terms of kernel functions. T ..."
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Cited by 4 (0 self)
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Abstract: We present two estimators for discrete nonGaussian and nonstationary probability density estimation based on a dynamic Bayesian network (DBN). The first estimator is for offline computation and consists of a DBN whose transition distribution is represented in terms of kernel functions. The estimator parameters are the weights and shifts of the kernel functions. The parameters are determined through a recursive learning algorithm using maximum likelihood (ML) estimation. The second estimator is a DBN whose parameters form the transition probabilities. We use an asymptotically convergent, recursive, online algorithm to update the parameters using observation data. The DBN calculates the state probabilities using the estimated parameters. We provide examples that demonstrate the usefulness and simplicity of the two proposed estimators.
Identifying Humans by Their Walk and Generating New Motions Using Hidden Markov Models
"... The problems we consider in this work are that of recognizing a human figure from 3D motion capture data of a person walking, and also of generating new movement data useful for movies, simulations or ..."
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Cited by 3 (0 self)
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The problems we consider in this work are that of recognizing a human figure from 3D motion capture data of a person walking, and also of generating new movement data useful for movies, simulations or
Open Access
, 2005
"... Research article Trends in physical activity and inactivity amongst US 14–18 year olds by gender, school grade and race, 1993–2003: evidence from the youth risk behavior survey ..."
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Research article Trends in physical activity and inactivity amongst US 14–18 year olds by gender, school grade and race, 1993–2003: evidence from the youth risk behavior survey
Using Bayesian Networks in an Industrial Setting: Making Printing Systems Adaptive
"... Abstract. Control engineering is a field of major industrial importance as it offers principles for engineering controllable physical devices, such as cell phones, television sets, and printing systems. Control engineering techniques assume that a physical system’s dynamic behaviour can be completel ..."
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Cited by 3 (3 self)
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Abstract. Control engineering is a field of major industrial importance as it offers principles for engineering controllable physical devices, such as cell phones, television sets, and printing systems. Control engineering techniques assume that a physical system’s dynamic behaviour can be completely described by means of a set of equations. However, as modern systems are often of high complexity, drafting such equations has become more and more difficult. Moreover, to dynamically adapt the system’s behaviour to a changing environment, observations obtained from sensors at runtime need to be taken into account. However, such observations give an incomplete picture of the system’s behaviour; when combined with the incompletely understood complexity of the device, control engineering solutions increasingly fall short. Probabilistic reasoning would allow one to deal with these sources of incompleteness, yet in the area of control engineering such AI solutions are rare. When using a Bayesian network in this context the required model can be learnt, and tuned, from data, uncertainty can be handled, and the model can be subsequently used for stochastic control of the system’s behaviour. In this paper we discuss industrial research in which Bayesian networks were successfully used to control complex printing systems. 1