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Poster
Infinite State Bayes-Nets for Structured Domains
Max Welling · Ian Porteous · Evgeniy Bart
Author Information
Max Welling (Microsoft Research AI4Science / University of Amsterdam)
Ian Porteous (University of California Irvine)
Evgeniy Bart (Cal Tech)
Related Events (a corresponding poster, oral, or spotlight)
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2007 Spotlight: Infinite State Bayes-Nets for Structured Domains »
Tue. Dec 4th 05:50 -- 06:00 PM Room
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2019 : TBD »
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2019 : Keynote - ML »
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2017 : Panel Session »
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2017 : Panel: On the Foundations and Future of Approximate Inference »
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2017 : Invited talk 1: Deep recurrent inverse modeling for radio astronomy and fast MRI imaging »
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2017 Poster: Causal Effect Inference with Deep Latent-Variable Models »
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2017 Poster: Bayesian Compression for Deep Learning »
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2016 : Max Welling : Making Deep Learning Efficient Through Sparsification »
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2016 Poster: Improving Variational Autoencoders with Inverse Autoregressive Flow »
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2015 Workshop: Scalable Monte Carlo Methods for Bayesian Analysis of Big Data »
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2015 : *Max Welling* Optimization Monte Carlo »
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2015 Symposium: Deep Learning Symposium »
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2015 Poster: Bayesian dark knowledge »
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2015 Poster: Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference »
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2014 Workshop: ABC in Montreal »
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2014 Poster: Semi-supervised Learning with Deep Generative Models »
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2014 Demonstration: Machine Learning in the Browser »
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2014 Spotlight: Semi-supervised Learning with Deep Generative Models »
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2013 Workshop: Probabilistic Models for Big Data »
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2012 Poster: The Time-Marginalized Coalescent Prior for Hierarchical Clustering »
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2011 Poster: Statistical Tests for Optimization Efficiency »
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2010 Poster: On Herding and the Perceptron Cycling Theorem »
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2008 Session: Oral session 10: Nonparametric Processes, Scene Processing and Image Statistics »
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2006 Poster: Structure Learning in Markov Random Fields »
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2006 Spotlight: Accelerated Variational Dirichlet Process Mixtures »
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2006 Poster: A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation »
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