Matthew J. Johnson

According to our database1, Matthew J. Johnson authored at least 16 papers between 2010 and 2018.

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Bibliography

2018
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models.
CoRR, 2017

Structure-Exploiting variational inference for recurrent switching linear dynamical systems.
Proceedings of the 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2017

2016
Patterns of Scalable Bayesian Inference.
Foundations and Trends in Machine Learning, 2016

Composing graphical models with neural networks for structured representations and fast inference.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models.
CoRR, 2015

Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014
Bayesian time series models and scalable inference.
PhD thesis, 2014

Stochastic Variational Inference for Bayesian Time Series Models.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Bayesian nonparametric hidden semi-Markov models.
Journal of Machine Learning Research, 2013

Analyzing Hogwild Parallel Gaussian Gibbs Sampling.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012
A Simple Explanation of A Spectral Algorithm for Learning Hidden Markov Models
CoRR, 2012

The Hierarchical Dirichlet Process Hidden Semi-Markov Model
CoRR, 2012

2010
The Hierarchical Dirichlet Process Hidden Semi-Markov Model.
Proceedings of the UAI 2010, 2010

Necessary and sufficient conditions for high-dimensional salient feature subset recovery.
Proceedings of the IEEE International Symposium on Information Theory, 2010


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