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Dynamic Bayesian Networks: Representation, Inference and Learning by Kevin Patrick Murphy Doctor of Philosophy in Computer Science University of California, Berkeley Professor Stuart Russell, Chair Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for thisâ€¦ (More)

- Jeff A. Bilmes
- 1997

We describe the maximum-likelihood parameter estimation problem and how the ExpectationMaximization (EM) algorithm can be used for its solution. We first describe the abstract form of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture ofâ€¦ (More)

- Galen Andrew, Raman Arora, Jeff A. Bilmes, Karen Livescu
- ICML
- 2013

We introduce Deep Canonical Correlation Analysis (DCCA), a method to learn complex nonlinear transformations of two views of data such that the resulting representations are highly linearly correlated. Parameters of both transformations are jointly learned to maximize the (regularized) total correlation. It can be viewed as a nonlinear extension of theâ€¦ (More)

- Hui Lin, Jeff A. Bilmes
- ACL
- 2011

We design a class of submodular functions meant for document summarization tasks. These functions each combine two terms, one which encourages the summary to be representative of the corpus, and the other which positively rewards diversity. Critically, our functions are monotone nondecreasing and submodular, which means that an efficient scalable greedyâ€¦ (More)

- Chia-Ping Chen, Jeff A. Bilmes
- IEEE Transactions on Audio, Speech, and Languageâ€¦
- 2007

In this paper, we investigate a technique consisting of mean subtraction, variance normalization and time sequence filtering. Unlike other techniques, it applies auto-regression moving-average (ARMA) filtering directly in the cepstral domain. We call this technique mean subtraction, variance normalization, and ARMA filtering (MVA) post-processing, andâ€¦ (More)

- Jeff A. Bilmes, Katrin Kirchhoff
- HLT-NAACL
- 2003

We introduce factored language models (FLMs) and generalized parallel backoff (GPB). An FLM represents words as bundles of features (e.g., morphological classes, stems, data-driven clusters, etc.), and induces a probability model covering sequences of bundles rather than just words. GPB extends standard backoff to general conditional probability tablesâ€¦ (More)

- Jeff A. Bilmes, Krste Asanovic, Chee-Whye Chin, James Demmel
- International Conference on Supercomputing
- 1997

PHiPAC was an early attempt to improve software performance by searching in a large design space of possible implementations to find the best one. At the time, in the early 1990s, the most efficient numerical linear algebra libraries were carefully hand tuned for specific microarchitectures and compilers, and were often written in assembly language. Thisâ€¦ (More)

- Hui Lin, Jeff A. Bilmes
- HLT-NAACL
- 2010

We treat the text summarization problem as maximizing a submodular function under a budget constraint. We show, both theoretically and empirically, a modified greedy algorithm can efficiently solve the budgeted submodular maximization problem near-optimally, and we derive new approximation bounds in doing so. Experiments on DUCâ€™04 task show that ourâ€¦ (More)

- Jeff A. Bilmes, Geoffrey Zweig
- 2002 IEEE International Conference on Acousticsâ€¦
- 2002

This paper describes the Graphical Models Toolkit (GMTK), an open source, publically available toolkit for developing graphical-model based speech recognition and general time series systems. Graphical models are a flexible, concise, and expressive probabilistic modeling framework with which one may rapidly specify a vast collection of statistical models.â€¦ (More)

- Michael M. Hoffman, Orion J. Buske, Jie Wang, Zhiping Weng, Jeff A. Bilmes, William Stafford Noble
- Nature Methods
- 2012

Sequence census methods like ChIP-seq now produce an unprecedented amount of genome-anchored data. We have developed an integrative method to identify patterns from multiple experiments simultaneously while taking full advantage of high-resolution data, discovering joint patterns across different assay types. We apply this method to ENCODE chromatin dataâ€¦ (More)