Jeff A. Bilmes

Xiao Li, Jeff A. Bilmes, Jonathan Malkin, 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

Predicting Nucleosome Positioning Using Multiple Evidence Tracks

Jeff A. Bilmes, William Stafford Noble, Zhiping Weng, 2010, RECOMB.

Factored sparse inverse covariance matrices

Jeff A. Bilmes, J. Bilmes, 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

An online adaptive filtering algorithm for the vocal joystick

Richard Wright, Xiao Li, Jeff A. Bilmes, 2006, INTERSPEECH.

Submodularity for Data Selection in Statistical Machine Translation

Jeff A. Bilmes, Katrin Kirchhoff, J. Bilmes, 2014 .

On Bisubmodular Maximization

Jeff A. Bilmes, Andrew Guillory, Ajit P. Singh, 2012, AISTATS.

Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers

Jeff A. Bilmes, Jennifer Chu-Carroll, Mark Sanderson, 2006, HLT-NAACL 2006.

Improving multi-lattice alignment based spoken keyword spotting

Hui Lin, Jeff A. Bilmes, Alex Stupakov, 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

Online Submodular Set Cover, Ranking, and Repeated Active Learning

Jeff A. Bilmes, Andrew Guillory, J. Bilmes, 2011, NIPS.

Polyphase speech recognition

Hui Lin, Jeff A. Bilmes, 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

Algorithms for Optimizing the Ratio of Submodular Functions

Rishabh K. Iyer, Jeff A. Bilmes, Kai Wei, 2016, ICML.

Submodularity and adaptation

Jeff A. Bilmes, 2007, 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU).

Learning Graphical Models over partial k-trees

Jeff A. Bilmes, Mukund Narasimhan, J. Bilmes, 2004 .

Submodularity Cuts and Applications

Jeff A. Bilmes, Yoshinobu Kawahara, Koji Tsuda, 2009, NIPS.

Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions

Jeff A. Bilmes, William Stafford Noble, Wenruo Bai, 2018, NeurIPS.

Summarization of Multi-Document Topic Hierarchies using Submodular Mixtures

Rishabh K. Iyer, Ganesh Ramakrishnan, Jeff A. Bilmes, 2015, ACL.

The RAP: a ring array processor for layered network calculations

Jeff A. Bilmes, Eric Allman, Nelson Morgan, 1990, [1990] Proceedings of the International Conference on Application Specific Array Processors.

The 2001 GMTK-based SPINE ASR system

Jeff A. Bilmes, Mari Ostendorf, Harriet J. Nock, 2002, INTERSPEECH.

DBN-based multi-stream models for Mandarin toneme recognition

Jeff A. Bilmes, Mari Ostendorf, Gang Ji, 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

Morphology-based language modeling for conversational Arabic speech recognition

Andreas Stolcke, Kevin Duh, Jeff A. Bilmes, 2006, Computer Speech and Language.

Concurrent Real-Time Music in C++

David P. Anderson, Jeff A. Bilmes, 1991, C++ Conference.

Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification

Jeff A. Bilmes, Amarnag Subramanya, J. Bilmes, 2009, NIPS.

Modelling the prepausal lengthening effect for speech recognition: a dynamic Bayesian network approach

Ning Ma, Phil D. Green, Jeff A. Bilmes, 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra

Jeff A. Bilmes, William Stafford Noble, Katrin Kirchhoff, 2012, UAI.

How to loose confidence: probabilistic linear machines for multiclass classification

Hui Lin, Koby Crammer, Jeff A. Bilmes, 2009, INTERSPEECH.

Backoff Model Training using Partially Observed Data: Application to Dialog Act Tagging

Jeff A. Bilmes, Gang Ji, J. Bilmes, 2006, NAACL.

Submodular-Bregman and the Lovász-Bregman Divergences with Applications

Rishabh K. Iyer, Jeff A. Bilmes, J. Bilmes, 2012, NIPS.

On Approximate Non-submodular Minimization via Tree-Structured Supermodularity

Rishabh K. Iyer, Jeff A. Bilmes, Yoshinobu Kawahara, 2015, AISTATS.

A Submodular-supermodular Procedure with Applications to Discriminative Structure Learning pdf

Jeff A. Bilmes, Mukund Narasimhan, J. Bilmes, 2005, UAI.

Part-of-Speech Tagging using Virtual Evidence and Negative Training

Jeff A. Bilmes, Sheila M. Reynolds, Sheila M. Reynolds, 2005, HLT.

A high-speed, low-resource ASR back-end based on custom arithmetic

Xiao Li, Jeff A. Bilmes, Jonathan Malkin, 2006, IEEE Transactions on Audio, Speech, and Language Processing.

Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (2009) pdf

Jeff A. Bilmes, Andrew Y. Ng, J. Bilmes, 2012, ArXiv.

PAC-learning Bounded Tree-width Graphical Models

Jeff A. Bilmes, Mukund Narasimhan, J. Bilmes, 2004, UAI.

A Factored Language Model of Quantized Pitch and Duration

Xiao Li, Jeff A. Bilmes, Gang Ji, 2006, ICMC.

A weakly supervised activity recognition framework for real-time synthetic biology laboratory assistance

Jeff A. Bilmes, Sunil Thulasidasan, Chandrashekhar Lavania, 2016, UbiComp.

Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity

Jeff A. Bilmes, Tianyi Zhou, J. Bilmes, 2018, ICLR.

Dialog act tagging using graphical models

Jeff A. Bilmes, Gang Ji, J. Bilmes, 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

On Deep Multi-View Representation Learning pdf

Jeff A. Bilmes, Raman Arora, Karen Livescu, 2015, ICML.

Panlingual lexical translation via probabilistic inference

Oren Etzioni, Jeff A. Bilmes, Mausam, 2010, Artif. Intell..

Use of syllable nuclei locations to improve ASR

Jeff A. Bilmes, Chris D. Bartels, 2007, 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU).

Multi-Speaker Language Modeling

Jeff A. Bilmes, Gang Ji, 2004, HLT-NAACL.

Joint distributional modeling with cross-correlation based features

Jeff A. Bilmes, J. Bilmes, 1997, 1997 IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings.

Genetic triangulation of graphical models for speech and language processing

Kevin Duh, Simon King, Jeff A. Bilmes, 2005, INTERSPEECH.

Stream Clipper: Scalable Submodular Maximization on Stream pdf

Jeff A. Bilmes, Tianyi Zhou, J. Bilmes, 2016, ArXiv.

Online algorithms for submodular minimization with combinatorial constraints

Jeff A. Bilmes, Stefanie Jegelka, 2010, NIPS 2010.

Large Scale Graph Transduction

Jeff A. Bilmes, Amarnag Subramanya, J. Bilmes, 2009 .

Tied and Regularized Conditional Gaussian Graphical Models for Acoustic Modeling in ASR

Jeff A. Bilmes, J. Bilmes, 2006 .

Statistical acoustic indications of coarticulation

Jeff A. Bilmes, Katrin Kirchhoff, 1999 .

Graph-based submodular selection for extractive summarization

Hui Lin, Jeff A. Bilmes, Shasha Xie, 2009, 2009 IEEE Workshop on Automatic Speech Recognition & Understanding.

Virtual Evidence for Training Speech Recognizers Using Partially Labeled Data

Jeff A. Bilmes, Amarnag Subramanya, 2007, HLT-NAACL.

Analysis of the everyday human environment via large scale commonsense reasoning

Jeff A. Bilmes, William Pentney, 2008 .

How to select a good training-data subset for transcription: submodular active selection for sequences

Hui Lin, Jeff A. Bilmes, J. Bilmes, 2009, INTERSPEECH.

Using PHiPAC to speed error back-propagation learning

James Demmel, Jeff A. Bilmes, Krste Asanovic, 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Sequential Deep Belief Networks

Jeff A. Bilmes, Galen Andrew, 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Acoustic model transformations based on random projections

Jeff A. Bilmes, Tetsuya Takiguchi, Yasuo Ariki, 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Dynamic Bayesian Multinets

Jeff A. Bilmes, J. Bilmes, 2000, UAI.

Neurocomputing on the RAP

Jeff A. Bilmes, Nelson Morgan, P. Kohn, 1992 .

On Deep Multi-View Representation Learning

Jeff A. Bilmes, Raman Arora, Karen Livescu, 2015, ICML.

Deep Submodular Functions : Definitions & Learning

Jeff A. Bilmes, Brian Dolhansky, J. Bilmes, 2016 .

Evaluation of random-projection-based feature combination on speech recognition

Jeff A. Bilmes, Tetsuya Takiguchi, Yasuo Ariki, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

Selectively Computing Dynamic Features in the Likelihood Computation of ASR Systems

Xiao Li, Jeff A. Bilmes, 2003 .

Factored Language Models and Generalized Parallel Backoff

Jeff A. Bilmes, Katrin Kirchhoff, J. Bilmes, 2003, NAACL.

Submodular Bregman Divergences with Applications

Jeff A. Bilmes, J. Bilmes, 2012, NIPS 2012.

Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks

Jeff A. Bilmes, William Stafford Noble, Lukas Käll, 2008, PLoS Comput. Biol..

Towards the automated social analysis of situated speech data

Jeff A. Bilmes, Tanzeem Choudhury, Danny Wyatt, 2008, UbiComp.

Interactive Submodular Set Cover pdf

Jeff A. Bilmes, Andrew Guillory, J. Bilmes, 2010, ICML.

Compiling a Massive, Multilingual Dictionary via Probabilistic Inference

Oren Etzioni, Jeff A. Bilmes, Michael Skinner, 2009, ACL.

Hidden-articulator Markov models for speech recognition

Jeff A. Bilmes, Matthew Richardson, Chris Diorio, 2003, Speech Commun..

Speech Production in Speech Technologies: Introduction to the CSL Special Issue

Jeff A. Bilmes, Frank Rudzicz, Mark Hasegawa-Johnson, 2016, Comput. Speech Lang..

Discovering Long Range Properties of Social Networks with Multi-Valued Time-Inhomogeneous Models

Jeff A. Bilmes, Tanzeem Choudhury, Danny Wyatt, 2010, AAAI.

Stochastic perceptual speech models with durational dependence

Jeff A. Bilmes, Hervé Bourlard, Nelson Morgan, 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.

Graph Cuts with Interacting Edge Costs - Examples, Approximations, and Algorithms pdf

Jeff A. Bilmes, Stefanie Jegelka, J. Bilmes, 2014, ArXiv.

Techniques to Foster Drum Machine Expressivity

Jeff A. Bilmes, J. Bilmes, 1993, ICMC.

Analysis of Deep Neural Networks with Extended Data Jacobian Matrix

Jeff A. Bilmes, Matthew Richardson, Rich Caruana, 2016, ICML.

Generalized Graphical Abstractions for Statistical Machine Translation

Karim Filali, Jeff A. Bilmes, J. Bilmes, 2007, NAACL.

SVitchboard 1: small vocabulary tasks from Switchboard

Simon King, Jeff A. Bilmes, Chris D. Bartels, 2005, INTERSPEECH.

Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative Costs

Rishabh K. Iyer, Jeff A. Bilmes, J. Bilmes, 2019, AISTATS.

Classification of developmental disorders from speech signals using submodular feature selection

Jeff A. Bilmes, Yuzong Liu, Katrin Kirchhoff, 2013, INTERSPEECH.

Divide-and-Conquer Learning by Anchoring a Conical Hull

Jeff A. Bilmes, Carlos Guestrin, Tianyi Zhou, 2014, NIPS.

Prioritizing transcriptomic and epigenomic experiments using an optimization strategy that leverages imputed data

Jeff A. Bilmes, William Stafford Noble, Jacob M. Schreiber, 2019, bioRxiv.

What HMMs Can Do

Jeff A. Bilmes, J. Bilmes, 2006, IEICE Trans. Inf. Syst..

Unsupervised pattern discovery in human chromatin structure through genomic segmentation

Jie Wang, Jeff A. Bilmes, William Stafford Noble, 2013, BCB.

A novel representation for rhythmic structure

Jeff A. Bilmes, David Wessel, Vijay Iyer, 1997 .

Non-Minimal Triangulations for Mixed Stochastic/Deterministic Graphical Models pdf

Jeff A. Bilmes, Chris D. Bartels, J. Bilmes, 2006, UAI.

The Ring Array Processor: A Multiprocessing Peripheral for Connection Applications

Jeff A. Bilmes, Eric Allman, Nelson Morgan, 1992, J. Parallel Distributed Comput..

A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems

Rishabh K. Iyer, Jeff A. Bilmes, J. Bilmes, 2019, AISTATS.

Ring Array Processor (RAP): Software Architecture

Jeff A. Bilmes, Philip D. Kohn, 1991 .

Rao-Blackwellized Particle Filters for Recognizing Activities and Spatial Context from Wearable Sensors

Jeff A. Bilmes, Dieter Fox, Amarnag Subramanya, 2006, ISER.

Entropic Graph-based Posterior Regularization

Jeff A. Bilmes, William Stafford Noble, Maxwell W. Libbrecht, 2015, ICML.

Hidden-articulator Markov models: performance improvements and robustness to noise

Jeff A. Bilmes, Matthew Richardson, Chris Diorio, 2000, INTERSPEECH.

Maximum margin learning and adaptation of MLP classifiers

Xiao Li, Jeff A. Bilmes, Jonathan Malkin, 2005, INTERSPEECH.

Object class recognition using images of abstract regions

Yi Li, Jeff A. Bilmes, Linda G. Shapiro, 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

Approximation Bounds for Inference using Cooperative Cuts

Jeff A. Bilmes, Stefanie Jegelka, J. Bilmes, 2011, ICML.

Local Search for Balanced Submodular Clusterings

Jeff A. Bilmes, Mukund Narasimhan, J. Bilmes, 2007, IJCAI.

SVitchboard II and fiSVer i: high-quality limited-complexity corpora of conversational English speech

Rishabh K. Iyer, Jeff A. Bilmes, Yuzong Liu, 2015, INTERSPEECH.

Submodularity for Data Selection in Machine Translation

Jeff A. Bilmes, Katrin Kirchhoff, J. Bilmes, 2014, EMNLP.

Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers

Franz Pernkopf, Jeff A. Bilmes, J. Bilmes, 2010, J. Mach. Learn. Res..

Knows When it Doesn’t Know: Deep Abstaining Classifiers

Jeff A. Bilmes, Gopinath Chennupati, Sunil Thulasidasan, 2018 .

Optimization on Seperator Trees

Jeff A. Bilmes, Mukund Narasimhan, J. Bilmes, 2004 .

Learning Mixtures of Submodular Shells with Application to Document Summarization

Hui Lin, Jeff A. Bilmes, J. Bilmes, 2012, UAI.

Frontend post-processing and backend model enhancement on the Aurora 2.0/3.0 databases

Karim Filali, Jeff A. Bilmes, Chia-Ping Chen, 2002, INTERSPEECH.

Multi-dynamic Bayesian Networks

Karim Filali, Jeff A. Bilmes, J. Bilmes, 2006, NIPS.

Impact of Signal Processing and of Our Work

Vikram Krishnamurthy, Brendan J. Frey, Jeff A. Bilmes, 2010 .

WHAT HMMS CAN'T DO

Jeff A. Bilmes, J. Bilmes, 2004 .

Maximum mutual information based reduction strategies for cross-correlation based joint distributional modeling

Jeff A. Bilmes, J. Bilmes, 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

Codebook design for ASR systems using custom arithmetic units

Xiao Li, Jeff A. Bilmes, Jonathan Malkin, 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

COSINE - A corpus of multi-party COnversational Speech In Noisy Environments

Jeff A. Bilmes, Dieter Fox, Evan Hanusa, 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

Q-Clustering

Jeff A. Bilmes, Mukund Narasimhan, Nebojsa Jojic, 2005, NIPS.

Jumpout: Improved Dropout for Deep Neural Networks with Rectified Linear Units

Jeff A. Bilmes, Shengjie Wang, Tianyi Zhou, 2018 .

Fast Semidifferential-based Submodular Function Optimization

Rishabh K. Iyer, Jeff A. Bilmes, Stefanie Jegelka, 2013, ICML.

Mixed-memory Markov models for Automatic Language Identification

Jeff A. Bilmes, Katrin Kirchhoff, Sonia Parandekar, 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Author retrospective for optimizing matrix multiply using PHiPAC: a portable high-performance ANSI C coding methodology

James Demmel, Jeff A. Bilmes, Krste Asanovic, 2014, ICS 25th Anniversary.

Faster and more accurate graphical model identification of tandem mass spectra using trellises

Jeff A. Bilmes, William Stafford Noble, Shengjie Wang, 2016, Bioinform..

On fast approximate submodular minimization

Hui Lin, Jeff A. Bilmes, Stefanie Jegelka, 2011, NIPS.

Training Compressed Fully-Connected Networks with a Density-Diversity Penalty

Jeff A. Bilmes, William Stafford Noble, Shengjie Wang, 2016, International Conference on Learning Representations.

Semi-Supervised Learning with Measure Propagation

Jeff A. Bilmes, Amarnag Subramanya, J. Bilmes, 2011, J. Mach. Learn. Res..

Multi-scale deep tensor factorization learns a latent representation of the human epigenome

Jeff A. Bilmes, William Stafford Noble, Timothy Durham, 2018, bioRxiv.

Soft-Supervised Learning for Text Classification

Jeff A. Bilmes, Amarnag Subramanya, J. Bilmes, 2008, EMNLP.

Necessary Intransitive Likelihood-Ratio Classifiers

Jeff A. Bilmes, Gang Ji, J. Bilmes, 2003, NIPS.

Optimal sub-graphical models

Jeff A. Bilmes, Mukund Narasimhan, J. Bilmes, 2004, NIPS.

Creating non-minimal triangulations for use in inference in mixed stochastic/deterministic graphical models

Jeff A. Bilmes, Chris D. Bartels, J. Bilmes, 2011, Machine Learning.

DBN based multi-stream models for audio-visual speech recognition

Jeff A. Bilmes, Amarnag Subramanya, Chris D. Bartels, 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Bias Also Matters: Bias Attribution for Deep Neural Network Explanation

Jeff A. Bilmes, Shengjie Wang, Tianyi Zhou, 2019, ICML.

Segway 2.0 Application Note Scripts

Jeff A. Bilmes, William Stafford Noble, Maxwell W. Libbrecht, 2017 .

Inferring colocation and conversation networks from privacy-sensitive audio with implications for computational social science

Jeff A. Bilmes, Tanzeem Choudhury, Danny Wyatt, 2011, TIST.

A Privacy-Sensitive Approach to Modeling Multi-Person Conversations

Jeff A. Bilmes, Henry A. Kautz, Tanzeem Choudhury, 2007, IJCAI.

Computer Speech Processing EE516 Winter 2005

Jeff A. Bilmes, J. Bilmes, 2005 .

Submodularity in Data Subset Selection and Active Learning

Rishabh K. Iyer, Jeff A. Bilmes, Kai Wei, 2015, ICML.

The vocal joystick data collection effort and vowel corpus

Richard Wright, Xiao Li, Jeff A. Bilmes, 2006, INTERSPEECH.

Faster graphical model identification of tandem mass spectra using peptide word lattices

Jeff A. Bilmes, William Stafford Noble, Shengjie Wang, 2014, 1410.7875.

The graphical models toolkit: An open source software system for speech and time-series processing

Geoffrey Zweig, Jeff A. Bilmes, J. Bilmes, 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing

Jeff A. Bilmes, Andrew McCallum, Fernando Pereira, 2006 .

Disambiguating speech commands using physical context

Jeff A. Bilmes, James A. Landay, Susumu Harada, 2007, ICMI '07.

Ratio semi-definite classifiers

Jeff A. Bilmes, Jonathan Malkin, 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

Auto-Summarization: A Step Towards Unsupervised Learning of a Submodular Mixture

Jeff A. Bilmes, Chandrashekhar Lavania, J. Bilmes, 2019, SDM.

Graphical models and automatic speech recognition

Jeff A. Bilmes, 2002 .

Consensus ranking under the exponential model pdf

Jeff A. Bilmes, Marina Meila, Kapil Phadnis, 2007, UAI.

Regularized Adaptation of Discriminative Classifiers

Xiao Li, Jeff A. Bilmes, J. Bilmes, 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

Submodular Point Processes with Applications to Machine learning

Rishabh K. Iyer, Jeff A. Bilmes, J. Bilmes, 2015, AISTATS.

Discriminative versus generative parameter and structure learning of Bayesian network classifiers

Franz Pernkopf, Jeff A. Bilmes, J. Bilmes, 2005, ICML.

Semi-supervised learning for improved expression of uncertainty in discriminative classifiers

Jeff A. Bilmes, Jonathan Malkin, 2010, INTERSPEECH.

Smoothing class transitions with hard labels

Jeff A. Bilmes, Jonathan Malkin, 2010 .

Memory-efficient inference in dynamic graphical models using multiple cores

Jeff A. Bilmes, Galen Andrew, J. Bilmes, 2012, AISTATS.

Blind MVA Speech Feature Processing on Aurora 2.0

Daniel P. W. Ellis, Jeff A. Bilmes, Chia-Ping Chen, 2004 .

Scaling Submodular Maximization via Pruned Submodularity Graphs

Jeff A. Bilmes, Yi Chang, Hua Ouyang, 2016, AISTATS.

Graphical model approach to pitch tracking

Xiao Li, Jeff A. Bilmes, Jonathan Malkin, 2004, INTERSPEECH.

Jointly recognizing multi-speaker conversations

Jeff A. Bilmes, Gang Ji, J. Bilmes, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

Low-resource noise-robust feature post-processing on Aurora 2.0

Jeff A. Bilmes, Chia-Ping Chen, Katrin Kirchhoff, 2002, INTERSPEECH.

Speech feature smoothing for robust ASR

Daniel P. W. Ellis, Jeff A. Bilmes, Chia-Ping Chen, 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

GRAPHICAL MODEL REPRESENTATIONS OF WORD LATTICES

Jeff A. Bilmes, Christopher D. Manning, Gang Ji, 2006, 2006 IEEE Spoken Language Technology Workshop.

Order-based Discriminative Structure Learning for Bayesian Network Classifiers

Franz Pernkopf, Jeff A. Bilmes, J. Bilmes, 2008, ISAIM.

Label Selection on Graphs

Jeff A. Bilmes, Andrew Guillory, J. Bilmes, 2009, NIPS.

Dynamic classifier combination using utterance-level confidence values

Jeff A. Bilmes, Katrin Kirchhoff, 1999 .

The PHiPAC v1.0 Matrix-Multiply Distribution

James Demmel, Jeff A. Bilmes, Krste Asanovic, 1998 .

A Model for Musical Rhythm

Jeff A. Bilmes, 1992, ICMC.

DBN based multi-stream models for speech

Jeff A. Bilmes, Wei Hu, Shan Huang, 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

Natural statistical models for automatic speech recognition

Jeff A. Bilmes, Nelson Morgan, J. Bilmes, 1999 .

The Implementation of the Cell Tree: Design Alternatives and Performance Evaluation

Jeff A. Bilmes, Oliver Günther, J. Bilmes, 1989, BTW.

Multi-document Summarization via Budgeted Maximization of Submodular Functions

Hui Lin, Jeff A. Bilmes, J. Bilmes, 2010, NAACL.

The Vocal Joystick: A Voice-Based Human-Computer Interface for Individuals with Motor Impairments

Richard Wright, Xiao Li, Jeff A. Bilmes, 2005, HLT.

The design and collection of COSINE, a multi-microphone in situ speech corpus recorded in noisy environments

Jeff A. Bilmes, Dieter Fox, Evan Hanusa, 2012, Comput. Speech Lang..

Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications

Rishabh K. Iyer, Jeff A. Bilmes, Kai Wei, 2015, NIPS.

Fast Multi-stage Submodular Maximization

Rishabh K. Iyer, Jeff A. Bilmes, Kai Wei, 2014, ICML.

MVA Processing of Speech Features

Jeff A. Bilmes, Chia-Ping Chen, J. Bilmes, 2007, IEEE Transactions on Audio, Speech, and Language Processing.

SVitchboard-II and FiSVer-I: Crafting high quality and low complexity conversational english speech corpora using submodular function optimization

Rishabh K. Iyer, Jeff A. Bilmes, Yuzong Liu, 2017, Comput. Speech Lang..

Constrained Interacting Submodular Groupings

Maya R. Gupta, Jeff A. Bilmes, Andrew Cotter, 2018, ICML.

On Triangulating Dynamic Graphical Models

Jeff A. Bilmes, Chris D. Bartels, J. Bilmes, 2002, UAI.

Dynamic Graphical Models

Jeff A. Bilmes, J. Bilmes, 2010, IEEE Signal Processing Magazine.

Introduction to the special issue on new computational paradigms for acoustic modeling in speech recognition

Jeff A. Bilmes, Martin J. Russell, J. Bilmes, 2003, Comput. Speech Lang..

Polyhedral aspects of Submodularity, Convexity and Concavity pdf

Rishabh K. Iyer, Jeff A. Bilmes, J. Bilmes, 2015, ArXiv.

A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data

Jeff A. Bilmes, Xiaoyu Chen, William Stafford Noble, 2010, Bioinform..

Recognizing Activities and Spatial Context Using Wearable Sensors pdf

Jeff A. Bilmes, Dieter Fox, Amarnag Subramanya, 2006, UAI.

A Bayesian Divergence Prior for Classiffier Adaptation

Xiao Li, Jeff A. Bilmes, J. Bilmes, 2007, AISTATS.

Submodularity beyond submodular energies: Coupling edges in graph cuts

Jeff A. Bilmes, Stefanie Jegelka, J. Bilmes, 2011, CVPR 2011.

The semi-supervised switchboard transcription project

Jeff A. Bilmes, Amarnag Subramanya, J. Bilmes, 2009, INTERSPEECH.

Online adaptive learning for speech recognition decoding

Hui Lin, Jeff A. Bilmes, J. Bilmes, 2010, INTERSPEECH.

Graphical models for integrating syllabic information

Jeff A. Bilmes, Chris D. Bartels, J. Bilmes, 2010, Comput. Speech Lang..

Active Semi-Supervised Learning using Submodular Functions

Jeff A. Bilmes, Andrew Guillory, J. Bilmes, 2011, UAI.

Submodular feature selection for high-dimensional acoustic score spaces

Jeff A. Bilmes, Kai Wei, Yuzong Liu, 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

Regularized adaptation: theory, algorithms and applications

Xiao Li, Jeff A. Bilmes, J. Bilmes, 2007 .

Dynamic classifier combination in hybrid speech recognition systems using utterance-level confidence values

Jeff A. Bilmes, Katrin Kirchhoff, 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

Demo of VJ-Voicebot: control of robotic arm with the Vocal Joystick

Jeff A. Bilmes, Jonathan Malkin, Brandi House, 2007, Assets '07.

Feature pruning in likelihood evaluation of HMM-based speech recognition

Xiao Li, Jeff A. Bilmes, 2003, 2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721).

Unsupervised learning of acoustic features via deep canonical correlation analysis

Jeff A. Bilmes, Raman Arora, Karen Livescu, 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure

Jeff A. Bilmes, Zafer Aydin, William Stafford Noble, 2011, BMC Bioinformatics.

Robust splicing costs and efficient search with BMM Models for concatenative speech synthesis

Jeff A. Bilmes, Mari Ostendorf, Ivan Bulyko, 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Multi-dynamic bayesian networks for machine translation and nlp

Karim Filali, Jeff A. Bilmes, 2007 .

Submodular Hamming Metrics

Rishabh K. Iyer, Jeff A. Bilmes, Jennifer Gillenwater, 2015, NIPS.

Applications of virtual-evidence based speech recognizer training

Jeff A. Bilmes, Amarnag Subramanya, 2008, INTERSPEECH.

Graphical models for large vocabulary speech recognition

Jeff A. Bilmes, Chris Bartels, 2008 .

Hidden feature models for speech recognition using dynamic Bayesian networks

James R. Glass, Jeff A. Bilmes, Karen Livescu, 2003, INTERSPEECH.

Instructions for HLT-NAACL 2006 Proceedings

Jeff A. Bilmes, Jennifer Chu-Carroll, Mark Sanderson, 2006 .

Monotone Closure of Relaxed Constraints in Submodular Optimization: Connections Between Minimization and Maximization

Rishabh K. Iyer, Jeff A. Bilmes, Stefanie Jegelka, 2014, UAI.

A learned embedding for efficient joint analysis of millions of mass spectra

Jeff A. Bilmes, William Stafford Noble, Damon May, 2018, Nature Methods.

A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models

Jeff A. Bilmes, J. Bilmes, L. Cranor, 1998 .

Hierarchical Models for Activity Recognition

Jeff A. Bilmes, Dieter Fox, Amarnag Subramanya, 2006, 2006 IEEE Workshop on Multimedia Signal Processing.

Software for ANN Training on a Ring Array Processor

Jeff A. Bilmes, Nelson Morgan, James Beck, 1991, NIPS.

Feature pruning for low-power ASR systems in clean and noisy environments

Xiao Li, Jeff A. Bilmes, 2005, IEEE Signal Processing Letters.

Unsupervised submodular subset selection for speech data

Jeff A. Bilmes, Kai Wei, Yuzong Liu, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry

Jeff A. Bilmes, William Stafford Noble, John T. Halloran, 2014, UAI.

Curriculum Learning by Dynamic Instance Hardness

Jeff A. Bilmes, Shengjie Wang, Tianyi Zhou, 2020, NeurIPS.

Learning a Weighted Sequence Model of the Nucleosome Core and Linker Yields More Accurate Predictions in Saccharomyces cerevisiae and Homo sapiens

Jeff A. Bilmes, William Stafford Noble, Sheila M. Reynolds, 2010, PLoS Comput. Biol..

Generalized rules for combination and joint training of classifiers

Jeff A. Bilmes, Katrin Kirchhoff, J. Bilmes, 2003, Pattern Analysis & Applications.

Conversation detection and speaker segmentation in privacy-sensitive situated speech data

Jeff A. Bilmes, Tanzeem Choudhury, Danny Wyatt, 2007, INTERSPEECH.

Factored Language Models Tutorial

Kevin Duh, Jeff A. Bilmes, Katrin Kirchhoff, 2007 .

Learning Hidden Curved Exponential Family Models to Infer Face-to-Face Interaction Networks from Situated Speech Data

Jeff A. Bilmes, Tanzeem Choudhury, Danny Wyatt, 2008, AAAI.

Object class recognition using images of abstract regions

Jeff A. Bilmes, Linda G. Shapiro, Yi Li, 2004, ICPR 2004.

A Dynamic Bayesian Framework to Model Context and Memory in Edit Distance Learning: An Application to Pronunciation Classification

Karim Filali, Jeff A. Bilmes, J. Bilmes, 2005, ACL.

Multi-label cooperative cuts

Jeff A. Bilmes, Stefanie Jegelka, 2011, CVPR 2011.

Optimal Selection of Limited Vocabulary Speech Corpora

Hui Lin, Jeff A. Bilmes, 2011, INTERSPEECH.

Focused word segmentation for ASR

Jeff A. Bilmes, Amarnag Subramanya, Chia-Ping Chen, 2005, INTERSPEECH.

Simultaneous Learning and Covering with Adversarial Noise

Jeff A. Bilmes, Andrew Guillory, J. Bilmes, 2011, ICML.

Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions

Rishabh K. Iyer, Jeff A. Bilmes, Stefanie Jegelka, 2013, NIPS.

Buried Markov models for speech recognition

Jeff A. Bilmes, J. Bilmes, 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

Learning Large Scale Common Sense Models of Everyday Life

Jeff A. Bilmes, Henry A. Kautz, Matthai Philipose, 2007, AAAI.

Segway 2.0 Application Note Datasets

Jeff A. Bilmes, William Stafford Noble, Maxwell W. Libbrecht, 2017 .

Jumpout : Improved Dropout for Deep Neural Networks with ReLUs

Jeff A. Bilmes, Shengjie Wang, Tianyi Zhou, 2019, ICML.

Online Submodular Minimization for Combinatorial Structures

Jeff A. Bilmes, Stefanie Jegelka, J. Bilmes, 2011, ICML.

Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology

James Demmel, Jeff A. Bilmes, Krste Asanovic, 1997, ICS '97.

Directed graphical models of classifier combination: application to phone recognition

Jeff A. Bilmes, Katrin Kirchhoff, J. Bilmes, 2000, INTERSPEECH.

Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, June 4-9, 2006, New York, New York, USA

Jeff A. Bilmes, Jennifer Chu-Carroll, Mark Sanderson, 2006, HLT-NAACL.

The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking pdf

Rishabh K. Iyer, Jeff A. Bilmes, J. Bilmes, 2013, UAI.

Spoken keyword spotting via multi-lattice alignment

Hui Lin, Jeff A. Bilmes, Alex Stupakov, 2008, INTERSPEECH.

Reducing total latency in online real-time inference and decoding via combined context window and model smoothing latencies

Jeff A. Bilmes, Chandrashekhar Lavania, 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

The vocal joystick:: evaluation of voice-based cursor control techniques

Xiao Li, Jeff A. Bilmes, James A. Landay, 2006, Assets '06.

Learning Mixtures of Submodular Functions for Image Collection Summarization

Rishabh K. Iyer, Sebastian Tschiatschek, Jeff A. Bilmes, 2014, NIPS.

Practical Methods for Exploiting Bounds on Change in the Margin

Jeff A. Bilmes, Andrew Guillory, 2008, ISAIM.

Structurally discriminative graphical models for automatic speech recognition - results from the 2001 Johns Hopkins Summer Workshop

Geoffrey Zweig, Karim Filali, Jeff A. Bilmes, 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Data-driven vector clustering for low-memory footprint ASR

Karim Filali, Xiao Li, Jeff A. Bilmes, 2002, INTERSPEECH.

Data-driven extensions to HMM statistical dependencies

Jeff A. Bilmes, J. Bilmes, 1998, ICSLP.

Noise robustness in automatic speech recognition

Jeff A. Bilmes, Chia-Ping Chen, 2004 .

Statistical Models for Automatic Performance Tuning

James Demmel, Richard W. Vuduc, Jeff A. Bilmes, 2001, International Conference on Computational Science.

Using syllable nuclei locations to improve automatic speech recognition in the presence of burst noise

Jeff A. Bilmes, Chris D. Bartels, 2008, INTERSPEECH.

Uncertainty in artificial intelligence : proceedings of the Twenty-fifth Conference (2009) : June 18-21, 2009, Montreal, Quebec

Jeff A. Bilmes, Andrew Y. Ng, David McAllester, 2009 .

Structure Learning on Large Scale Common Sense Statistical Models of Human State

Jeff A. Bilmes, Matthai Philipose, William Pentney, 2008, AAAI.

Novel approaches to Arabic speech recognition: report from the 2002 Johns-Hopkins Summer Workshop

Jeff A. Bilmes, Dimitra Vergyri, Richard M. Schwartz, 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

The VoiceBot: a voice controlled robot arm

Jeff A. Bilmes, Jonathan Malkin, Brandi House, 2009, CHI.

Using mutual information to design feature combinations

Daniel P. W. Ellis, Jeff A. Bilmes, 2000, INTERSPEECH.

Constrained robust submodular sensor selection with applications to multistatic sonar arrays

Les E. Atlas, Jeff A. Bilmes, Thomas Powers, 2016, 2016 19th International Conference on Information Fusion (FUSION).

COMBINATION AND JOINT TRAINING OF ACOUSTIC CLASSIFIERS FOR SPEECH RECOGNITION

Jeff A. Bilmes, Katrin Kirchhoff, J. Bilmes, 2000 .

Word Alignment via Submodular Maximization over Matroids

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

Uncertainty in training large vocabulary speech recognizers

Jeff A. Bilmes, Patrick Nguyen, Amarnag Subramanya, 2007, 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU).