Kevin P. Murphy

发表

Using the forest to see the trees: exploiting context for visual object detection and localization

Antonio Torralba, Kevin P. Murphy, William T. Freeman, 2010, CACM.

Dynamic Bayesian Networks for Audio-Visual Speech Recognition

Kevin P. Murphy, Ara V. Nefian, Luhong Liang, 2002, EURASIP J. Adv. Signal Process..

Structure learning in random fields for heart motion abnormality detection

Mark W. Schmidt, Kevin P. Murphy, Glenn Fung, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

A non-myopic approach to visual search

Kevin P. Murphy, Julia Vogel, K. Murphy, 2007, Fourth Canadian Conference on Computer and Robot Vision (CRV '07).

Learning the Structure of Dynamic Probabilistic Networks pdf

Kevin P. Murphy, Stuart J. Russell, Nir Friedman, 1998, UAI.

Challenges and Solutions for Embedded and Networked Aerospace Software Systems Development of real-time component-based systems, model-based system development, and the use of automated code generation, simulation techniques and desktop test methods are described.

Kevin P. Murphy, David C. Sharp, Alex E. Bell, 2010 .

Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials

Mark W. Schmidt, Kevin P. Murphy, Kevin P. Murphy, 2010, AISTATS.

A Hybrid Conditional Random Field for Estimating the Underlying Ground Surface From Airborne LiDAR Data

James J. Little, Alla Sheffer, Kevin P. Murphy, 2009, IEEE Transactions on Geoscience and Remote Sensing.

Representing hierarchical POMDPs as DBNs for multi-scale robot localization

Leslie Pack Kaelbling, Kevin P. Murphy, Georgios Theocharous, 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

Multiscale Conditional Random Fields for Semi-supervised Labeling and Classification

Kevin P. Murphy, Benjamin M. Marlin, David Duvenaud, 2011, 2011 Canadian Conference on Computer and Robot Vision.

Sharing features: efficient boosting procedures for multiclass object detection

Antonio Torralba, Kevin P. Murphy, William T. Freeman, 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

Contextual Models for Object Detection Using Boosted Random Fields

Antonio Torralba, Kevin P. Murphy, William T. Freeman, 2004, NIPS.

A Survey of POMDP Solution Techniques

Kevin P. Murphy, K. Murphy, Kevin P. Murphy, 2000 .

Learning Graphical Model Structure Using L1-Regularization Paths

Mark W. Schmidt, Kevin P. Murphy, Alexandru Niculescu-Mizil, 2007, AAAI.

Bayesian structure learning using dynamic programming and MCMC pdf

Kevin P. Murphy, Daniel Eaton, K. Murphy, 2007, UAI.

A Variational Approximation for Bayesian Networks with Discrete and Continuous Latent Variables

Kevin P. Murphy, K. Murphy, Kevin P. Murphy, 1999, UAI.

Vision-based speaker detection using Bayesian networks

James M. Rehg, Paul W. Fieguth, Kevin P. Murphy, 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

Graphical Model For Recognizing Scenes and Objects.

Antonio Torralba, Kevin P. Murphy, William T. Freeman, 2003, NIPS 2003.

Sailing the Sea of Open Access: Celestial Navigation or Dead Reckoning?1

Kevin P. Murphy, Aaron B. Johnson, K. Murphy, 2020 .

Machine learning - a probabilistic perspective

Kevin P. Murphy, K. Murphy, Kevin P. Murphy, 2012, Adaptive computation and machine learning series.

Modeling Discrete Interventional Data using Directed Cyclic Graphical Models pdf

Mark W. Schmidt, Kevin P. Murphy, K. Murphy, 2009, UAI.

Causal learning without DAGs

Mark W. Schmidt, Kevin P. Murphy, David Duvenaud, 2008, NIPS Causality: Objectives and Assessment.

Linear-time inference in Hierarchical HMMs

Kevin P. Murphy, Mark A. Paskin, K. Murphy, 2001, NIPS.

Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models

Mohammad Emtiyaz Khan, Kevin P. Murphy, Benjamin M. Marlin, 2011, ICML.

Sequential Model-Based Parameter Optimization: an Experimental Investigation of Automated and Interactive Approaches

Thomas Bartz-Beielstein, Kevin P. Murphy, Kevin Leyton-Brown, 2010, Experimental Methods for the Analysis of Optimization Algorithms.

Sharing Visual Features for Multiclass and Multiview Object Detection

Antonio Torralba, Kevin P. Murphy, William T. Freeman, 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Bayesian Map Learning in Dynamic Environments

Kevin P. Murphy, K. Murphy, Kevin P. Murphy, 1999, NIPS.

Automata-Theoretic Models of Mutation and Alignment

David B. Searls, Kevin P. Murphy, K. Murphy, 1995, ISMB.

Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation

George Papandreou, Kevin P. Murphy, Alan L. Yuille, 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

A dynamic Bayesian network approach to figure tracking using learned dynamic models

Vladimir Pavlovic, James M. Rehg, Tat-Jen Cham, 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

Probability theory refresher

Kevin P. Murphy, K. Murphy, 2007 .

Loopy Belief Propagation for Approximate Inference: An Empirical Study

Michael I. Jordan, Kevin P. Murphy, Yair Weiss, 1999, UAI.

Time-Bounded Sequential Parameter Optimization

Kevin P. Murphy, Kevin Leyton-Brown, Holger H. Hoos, 2010, LION.

Integrating copy number polymorphisms into array CGH analysis using a robust HMM

Kevin P. Murphy, Raymond T. Ng, Sohrab P. Shah, 2006, ISMB.

Exact Bayesian structure learning from uncertain interventions

Kevin P. Murphy, Daniel Eaton, K. Murphy, 2007, AISTATS.

Modeling recurrent DNA copy number alterations in array CGH data

Kevin P. Murphy, Raymond T. Ng, Sohrab P. Shah, 2007, ISMB/ECCB.

Sparse Gaussian graphical models with unknown block structure

Kevin P. Murphy, Benjamin M. Marlin, 2009, ICML '09.

Model-based clustering of array CGH data

Kevin P. Murphy, Raymond T. Ng, Sohrab P. Shah, 2009, Bioinform..

A coupled HMM for audio-visual speech recognition

Kevin P. Murphy, Ara V. Nefian, Luhong Liang, 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (2012) pdf

Nando de Freitas, Kevin P. Murphy, 2013, ArXiv.

Figure-ground segmentation using a hierarchical conditional random field

Kevin P. Murphy, Jordan Reynolds, K. Murphy, 2007, Fourth Canadian Conference on Computer and Robot Vision (CRV '07).

Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models

Mohammad Emtiyaz Khan, Kevin P. Murphy, Benjamin M. Marlin, 2009, NIPS.

An experimental investigation of model-based parameter optimisation: SPO and beyond

Kevin P. Murphy, Kevin Leyton-Brown, Holger H. Hoos, 2009, GECCO.

Variational bounds for mixed-data factor analysis

Mohammad Emtiyaz Khan, Guillaume Bouchard, Kevin P. Murphy, 2010, NIPS.

The Factored Frontier Algorithm for Approximate Inference in DBNs pdf

Kevin P. Murphy, Yair Weiss, K. Murphy, 2001, UAI.

Semantic Instance Segmentation via Deep Metric Learning pdf

Peng Wang, Kevin P. Murphy, Sergio Guadarrama, 2017, ArXiv.

Context-based vision system for place and object recognition

Antonio Torralba, Kevin P. Murphy, William T. Freeman, 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

Modeling Uncertainty with Hedged Instance Embeddings

Seong Joon Oh, Kevin P. Murphy, Florian Schroff, 2019, ICLR.

Efficient parameter estimation for RNA secondary structure prediction

Kevin P. Murphy, Anne Condon, Holger H. Hoos, 2007, ISMB/ECCB.

A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models

Mohammad Emtiyaz Khan, Kevin P. Murphy, Benjamin M. Marlin, 2012, AISTATS.

Inference and Learning in Hybrid Bayesian Networks

Kevin P. Murphy, K. Murphy, 1998 .

Modeling changing dependency structure in multivariate time series

Kevin P. Murphy, Xiang Xuan, K. Murphy, 2007, ICML '07.

SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors

Kevin P. Murphy, Sohrab P. Shah, Ryan D. Morin, 2010, Bioinform..

Probabilistic Label Relation Graphs with Ising Models

Kevin P. Murphy, Hartmut Neven, Jia Deng, 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

Accelerated training of conditional random fields with stochastic gradient methods

Mark W. Schmidt, S. V. N. Vishwanathan, Kevin P. Murphy, 2006, ICML.

Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm

Mark W. Schmidt, Kevin P. Murphy, Michael P. Friedlander, 2009, AISTATS.

LabelMe: A Database and Web-Based Tool for Image Annotation

Antonio Torralba, Kevin P. Murphy, William T. Freeman, 2008, International Journal of Computer Vision.

Shared Features for Multiclass Object Detection

Antonio Torralba, Kevin P. Murphy, William T. Freeman, 2006, Toward Category-Level Object Recognition.

Group Sparse Priors for Covariance Estimation

Mark W. Schmidt, Kevin P. Murphy, Benjamin M. Marlin, 2009, UAI.

Object Detection and Localization Using Local and Global Features

Antonio Torralba, Kevin P. Murphy, William T. Freeman, 2006, Toward Category-Level Object Recognition.

Review of "Probabilistic graphical models" by Koller and Friedman

Kevin P. Murphy, K. Murphy, 2010, Artif. Intell..

Challenges and Solutions for Embedded and Networked Aerospace Software Systems

Kevin P. Murphy, David C. Sharp, Alex E. Bell, 2010, Proceedings of the IEEE.

Identifying players in broadcast sports videos using conditional random fields

James J. Little, Kevin P. Murphy, Jo-Anne Ting, 2011, CVPR 2011.

Learning to Track and Identify Players from Broadcast Sports Videos

James J. Little, Kevin P. Murphy, Jo-Anne Ting, 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Space-Efficient Inference in Dynamic Probabilistic Networks

Kevin P. Murphy, Stuart J. Russell, John Binder, 1997, IJCAI.

Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression

Mohammad Emtiyaz Khan, Kevin P. Murphy, Shakir Mohamed, 2012, NIPS.