no code implementations • ICML 2020 • Zhe Dong, Bryan A. Seybold, Kevin P. Murphy, Hung H. Bui
We propose an efficient inference method for switching nonlinear dynamical systems.
no code implementations • ICLR 2019 • Seong Joon Oh, Kevin P. Murphy, Jiyan Pan, Joseph Roth, Florian Schroff, Andrew C. Gallagher
Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering.
1 code implementation • 30 Mar 2017 • Alireza Fathi, Zbigniew Wojna, Vivek Rathod, Peng Wang, Hyun Oh Song, Sergio Guadarrama, Kevin P. Murphy
We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together.
no code implementations • ICCV 2015 • Austin Meyers, Nick Johnston, Vivek Rathod, Anoop Korattikara, Alex Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, Kevin P. Murphy
We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories.
1 code implementation • ICCV 2015 • George Papandreou, Liang-Chieh Chen, Kevin P. Murphy, Alan L. Yuille
Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation.
no code implementations • NeurIPS 2012 • Emtiyaz Khan, Shakir Mohamed, Kevin P. Murphy
We present a new variational inference algorithm for Gaussian processes with non-conjugate likelihood functions.
no code implementations • NeurIPS 2010 • Mohammad E. Khan, Guillaume Bouchard, Kevin P. Murphy, Benjamin M. Marlin
We show that EM is significantly more robust in the presence of missing data compared to treating the latent factors as parameters, which is the approach used by exponential family PCA and other related matrix-factorization methods.
no code implementations • NeurIPS 2009 • Baback Moghaddam, Emtiyaz Khan, Kevin P. Murphy, Benjamin M. Marlin
In this paper we make several contributions towards accelerating approximate Bayesian structural inference for non-decomposable GGMs.