K. Murphy

发表

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

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

GROUP SPARSITY VIA LINEAR-TIME PROJECTION

Mark W. Schmidt, K. Murphy, E. Berg, 2008 .

Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression

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

Automata-Theoretic Models of Mutation and Alignment

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

Modeling Discrete Interventional Data using Directed Cyclic Graphical Models pdf

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

Sequential Model-Based Parameter Optimisation: an Experimental Investigation of Automated and Inte

Thomas Bartz-Beielstein, Kevin Leyton-Brown, Holger H. Hoos, 2010 .

Machine learning - a probabilistic perspective

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

Efficient Bayesian Inference for Multivariate Probit Models With Sparse Inverse Correlation Matrices

A. Doucet, K. Murphy, A. Talhouk, 2012 .

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..

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.

Contextual Models for Object Detection Using Boosted Random Fields

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

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.

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.

Accelerated training of conditional random fields with stochastic gradient methods

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

Learning the Structure of Dynamic Probabilistic Networks pdf

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

Space-Efficient Inference in Dynamic Probabilistic Networks

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

Computational approaches for RNA energy parameter estimation.

K. Murphy, H. Hoos, D. Mathews, 2010, RNA.

Using the Forest to See the Trees: A Graphical Model Relating Features, Objects, and Scenes

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

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.

Sharing features: efficient boosting procedures for multiclass object detection

Antonio Torralba, William T. Freeman, Kevin Murphy, 2004, CVPR 2004.

Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models

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

Dynamic bayesian networks: representation, inference and learning

Stuart J. Russell, Kevin Murphy, Stuart Russell, 2002 .

Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks

Nando de Freitas, Arnaud Doucet, Kevin Murphy, 2000, UAI.

Loopy Belief Propagation for Approximate Inference: An Empirical Study

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

Bayes net toolbox for Matlab

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

Dynamic Bayesian Networks

Kevin Murphy, K. Murphy, 2002 .

Bayesian Map Learning in Dynamic Environments

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

Belief net structure learning from uncertain interventions

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

Causal learning without DAGs

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

Modeling changing dependency structure in multivariate time series

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

The Factored Frontier Algorithm for Approximate Inference in DBNs pdf

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

Variational bounds for mixed-data factor analysis

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

Passively Learning Finite Automata

K. Murphy, 1996 .

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

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

Time-Bounded Sequential Parameter Optimization

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

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

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

Object Detection and Localization Using Local and Global Features

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

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.

Linear-time inference in Hierarchical HMMs

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

Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior

K. Murphy, 2012 .

Models for generic visual object detection

K. Murphy, 2005 .

CRFs for estimating the underlying ground surface from airborne LiDAR data

K. Murphy, J. Little, Hongbo Fu, 2008 .

Representing hierarchical POMDPs as DBNs for multi-scale map learning

K. Murphy, L. Kaelbling, Georgios Theocharous, 2003 .

Bayesian statistics : a concise introduction

K. Murphy, Kevin P. Murphy, 2007 .

Semi-supervised Learning for Identifying Players from Broadcast Sports Videos with Play-byPlay Information

K. Murphy, J. Little, Jo-Anne Ting, 2011 .

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

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

Fast manipulation of multi-dimensional arrays in Matlab

K. Murphy, Kevin Murphy, 2001 .

Sharing features: efficient boosting procedures for multiclass object detection

A. Torralba, W. Freeman, K. Murphy, 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

Application of Bayesian Networks for Learner Assessment in E-Learning Systems

K. Murphy, M. Grigoriadou, H. Al-Sakran, 2019 .

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

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

Graphical Model For Recognizing Scenes and Objects.

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

Review of "Probabilistic graphical models" by Koller and Friedman

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

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).

A Survey of POMDP Solution Techniques

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

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.

Probability theory refresher

Kevin P. Murphy, K. Murphy, 2007 .

Inference and Learning in Hybrid Bayesian Networks

Kevin P. Murphy, K. Murphy, 1998 .

Overview of Machine Learning

K. Murphy, Kevin P. Murphy, Daniel Hsu, 2022, International Journal of Advanced Research in Science, Communication and Technology.

Modelling Gene Expression Data using Dynamic Bayesian Networks

Kevin Murphy, K. Murphy, Kevin P. Murphy, 2006 .

Exact Bayesian structure learning from uncertain interventions

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

Efficient parameter estimation for RNA secondary structure prediction

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

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.

Optimal Alignments in Linear Space Using Automaton-derived Cost Functions (extended Abstract)

K. Murphy, Kevin P. Murphy, 1996 .

An introduction to graphical models

Kevin P. Murphy, K. Murphy, 2011 .

Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning

K. Murphy, Zeel B Patel, Nipun Batra, 2022, ArXiv.

A brief introduction to graphical models and bayesian networks

Kevin Murphy, K. Murphy, Kevin P. Murphy, 1998 .

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.

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.

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.

Low-rank extended Kalman filtering for online learning of neural networks from streaming data

Alexander Y. Shestopaloff, K. Murphy, Peter Chang, 2023, ArXiv.

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

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

Bayesian structure learning using dynamic programming and MCMC pdf

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

Low-rank extended Kalman filtering for online learning of neural networks from streaming data

Alexander Y. Shestopaloff, K. Murphy, Gerardo Duran-Mart'in, 2023, 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).

Learning Graphical Model Structure Using L1-Regularization Paths

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

Identifying players in broadcast sports videos using conditional random fields

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

Shared Features for Multiclass Object Detection

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