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321
Dynamic Bayesian Networks: Representation, Inference and Learning
, 2002
"... Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and biosequence analysis, and KFMs have bee ..."
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Cited by 758 (3 self)
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Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and biosequence analysis, and KFMs have been used for problems ranging from tracking planes and missiles to predicting the economy. However, HMMs
and KFMs are limited in their “expressive power”. Dynamic Bayesian Networks (DBNs) generalize HMMs by allowing the state space to be represented in factored form, instead of as a single discrete random variable. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linearGaussian. In this thesis, I will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in DBNs, and how to learn DBN models from sequential data.
In particular, the main novel technical contributions of this thesis are as follows: a way of representing
Hierarchical HMMs as DBNs, which enables inference to be done in O(T) time instead of O(T 3), where T is the length of the sequence; an exact smoothing algorithm that takes O(log T) space instead of O(T); a simple way of using the junction tree algorithm for online inference in DBNs; new complexity bounds on exact online inference in DBNs; a new deterministic approximate inference algorithm called factored frontier; an analysis of the relationship between the BK algorithm and loopy belief propagation; a way of
applying RaoBlackwellised particle filtering to DBNs in general, and the SLAM (simultaneous localization
and mapping) problem in particular; a way of extending the structural EM algorithm to DBNs; and a variety of different applications of DBNs. However, perhaps the main value of the thesis is its catholic presentation of the field of sequential data modelling.
Bursty and Hierarchical Structure in Streams
, 2002
"... A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time. Email and news articles are two natural examples of such streams, each characterized by topics that appear, grow in intensity for a period of time, and then fade aw ..."
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Cited by 385 (2 self)
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A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time. Email and news articles are two natural examples of such streams, each characterized by topics that appear, grow in intensity for a period of time, and then fade away. The published literature in a particular research field can be seen to exhibit similar phenomena over a much longer time scale. Underlying much of the text mining work in this area is the following intuitive premise  that the appearance of a topic in a document stream is signaled by a "burst of activity," with certain features rising sharply in frequency as the topic emerges.
Recent advances in hierarchical reinforcement learning
, 2003
"... A preliminary unedited version of this paper was incorrectly published as part of Volume ..."
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Cited by 225 (25 self)
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A preliminary unedited version of this paper was incorrectly published as part of Volume
Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data
 IN ICML
, 2004
"... In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longrange dependencies exist. We present dynamic conditional random fields (DCRFs), a generalization of linearchain cond ..."
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Cited by 167 (13 self)
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In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longrange dependencies exist. We present dynamic conditional random fields (DCRFs), a generalization of linearchain conditional random fields (CRFs) in which each time slice contains a set of state variables and edgesa distributed state representation as in dynamic Bayesian networks (DBNs)and parameters are tied across slices. Since exact
Layered representations for learning and inferring office activity from multiple sensory channels
, 2004
"... ..."
Catching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization
, 2004
"... We consider the problem of modeling the content structure of texts within a specific domain, in terms of the topics the texts address and the order in which these topics appear. ..."
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Cited by 119 (7 self)
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We consider the problem of modeling the content structure of texts within a specific domain, in terms of the topics the texts address and the order in which these topics appear.
Linear Time Inference in Hierarchical HMMs
 In Proceedings of Neural Information Processing Systems
, 2001
"... The hierarchical hidden Markov model (HHMM) is a generalization of the hidden Markov model (HMM) that models sequences with structure at many length/time scales [FST98]. Unfortunately, the original inference algorithm is rather complicated, and takes O(T ) time, where T is the length of the s ..."
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Cited by 106 (3 self)
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The hierarchical hidden Markov model (HHMM) is a generalization of the hidden Markov model (HMM) that models sequences with structure at many length/time scales [FST98]. Unfortunately, the original inference algorithm is rather complicated, and takes O(T ) time, where T is the length of the sequence, making it impractical for many domains. In this paper, we show how HHMMs are a special kind of dynamic Bayesian network (DBN), and thereby derive a much simpler inference algorithm, which only takes O(T ) time. Furthermore, by drawing the connection between HHMMs and DBNs, we enable the application of many standard approximation techniques to further speed up inference.
A General Model for Online Probabilistic Plan Recognition
 In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI
, 2003
"... We present a new general framework for online probabilistic plan recognition called the Abstract Hidden Markov Memory Model (AHMEM). The new model is an extension of the existing Abstract Hidden Markov Model to allow the policy to have internal memory which can be updated in a Markov fashion. ..."
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Cited by 100 (1 self)
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We present a new general framework for online probabilistic plan recognition called the Abstract Hidden Markov Memory Model (AHMEM). The new model is an extension of the existing Abstract Hidden Markov Model to allow the policy to have internal memory which can be updated in a Markov fashion. We show that the AHMEM can represent a richer class of probabilistic plans, and at the same time derive an efficient algorithm for plan recognition in the AHMEM based on the RaoBlackwellised Particle Filter approximate inference method.
Learning and detecting activities from movement trajectories using the hierarchical hidden Markov models
 In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR
, 2005
"... Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both t ..."
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Cited by 81 (9 self)
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Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of realtime recognition, we propose a RaoBlackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the sharedstructure HHMM, the estimation of the model’s parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a realworld environment have confirmed our belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM. 1
Semantic Processing using the Hidden Vector State Model
 Computer Speech and Language
, 2005
"... This paper discusses semantic processing using the Hidden Vector State (HVS) model. The HVS model extends the basic discrete Markov model by encoding context in each state as a vector. State transitions are then factored into a stack shift operation similar to those of a pushdown automaton followed ..."
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Cited by 73 (26 self)
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This paper discusses semantic processing using the Hidden Vector State (HVS) model. The HVS model extends the basic discrete Markov model by encoding context in each state as a vector. State transitions are then factored into a stack shift operation similar to those of a pushdown automaton followed by a push of a new preterminal semantic category label. The key feature of the model is that it can capture hierarchical structure without the use of treebank data for training. Experiments have been conducted in the travel domain using the relatively simple ATIS corpus and the more complex DARPA Communicator Task. The results show that the HVS model can be robustly trained from only minimally annotated corpus data. Furthermore, when measured by its ability to extract attributevalue pairs from natural language queries in the travel domain, the HVS model outperforms a conventional finitestate semantic tagger by 4.1 % in Fmeasure for ATIS and by 6.6 % in Fmeasure for Communicator, suggesting that the benefit of the HVS model’s ability to encode context increases as the task becomes more complex.