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Poster
Accelerated Variational Dirichlet Process Mixtures
Kenichi Kurihara · Max Welling · Nikos Vlassis
Author Information
Kenichi Kurihara (Google)
Max Welling (University of Amsterdam / Qualcomm AI Research)
Nikos Vlassis (Netflix)
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2021 Poster: Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent »
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2021 Poster: Topographic VAEs learn Equivariant Capsules »
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2021 Poster: E(n) Equivariant Normalizing Flows »
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2021 Oral: E(n) Equivariant Normalizing Flows »
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2021 Poster: Modality-Agnostic Topology Aware Localization »
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2021 Poster: Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions »
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2021 Workshop: Deep Generative Models and Downstream Applications »
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2021 Workshop: Bayesian Deep Learning »
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2021 Workshop: AI for Science: Mind the Gaps »
Payal Chandak · Yuanqi Du · Tianfan Fu · Wenhao Gao · Kexin Huang · Shengchao Liu · Ziming Liu · Gabriel Spadon · Max Tegmark · Hanchen Wang · Adrian Weller · Max Welling · Marinka Zitnik -
2020 Poster: Natural Graph Networks »
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2020 Poster: SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks »
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2020 Poster: SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows »
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2020 Oral: SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows »
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2020 Poster: The Convolution Exponential and Generalized Sylvester Flows »
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2020 Poster: Bayesian Bits: Unifying Quantization and Pruning »
Mart van Baalen · Christos Louizos · Markus Nagel · Rana Ali Amjad · Ying Wang · Tijmen Blankevoort · Max Welling -
2020 Poster: Experimental design for MRI by greedy policy search »
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2020 Spotlight: Experimental design for MRI by greedy policy search »
Tim Bakker · Herke van Hoof · Max Welling -
2020 Poster: MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning »
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2019 Workshop: Bayesian Deep Learning »
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2019 Poster: Invert to Learn to Invert »
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2019 Poster: Deep Scale-spaces: Equivariance Over Scale »
Daniel Worrall · Max Welling -
2019 Poster: Integer Discrete Flows and Lossless Compression »
Emiel Hoogeboom · Jorn Peters · Rianne van den Berg · Max Welling -
2019 Poster: The Functional Neural Process »
Christos Louizos · Xiahan Shi · Klamer Schutte · Max Welling -
2019 Poster: Combining Generative and Discriminative Models for Hybrid Inference »
Victor Garcia Satorras · Zeynep Akata · Max Welling -
2019 Spotlight: Combining Generative and Discriminative Models for Hybrid Inference »
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2019 Poster: Combinatorial Bayesian Optimization using the Graph Cartesian Product »
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2018 Workshop: Bayesian Deep Learning »
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2018 Workshop: NIPS 2018 workshop on Compact Deep Neural Networks with industrial applications »
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2018 Poster: Graphical Generative Adversarial Networks »
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2018 Poster: 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data »
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2017 Workshop: Bayesian Deep Learning »
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2017 Workshop: Advances in Approximate Bayesian Inference »
Francisco Ruiz · Stephan Mandt · Cheng Zhang · James McInerney · James McInerney · Dustin Tran · Dustin Tran · David Blei · Max Welling · Tamara Broderick · Michalis Titsias -
2017 Poster: Causal Effect Inference with Deep Latent-Variable Models »
Christos Louizos · Uri Shalit · Joris M Mooij · David Sontag · Richard Zemel · Max Welling -
2017 Poster: Bayesian Compression for Deep Learning »
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2016 Workshop: Bayesian Deep Learning »
Yarin Gal · Christos Louizos · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2016 Poster: Improving Variational Autoencoders with Inverse Autoregressive Flow »
Diederik Kingma · Tim Salimans · Rafal Jozefowicz · Peter Chen · Xi Chen · Ilya Sutskever · Max Welling -
2016 Poster: A posteriori error bounds for joint matrix decomposition problems »
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2015 Workshop: Scalable Monte Carlo Methods for Bayesian Analysis of Big Data »
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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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2015 Poster: Variational Dropout and the Local Reparameterization Trick »
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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 »
Ted Meeds · Remco Hendriks · Said Al Faraby · Magiel Bruntink · Max Welling -
2014 Spotlight: Semi-supervised Learning with Deep Generative Models »
Diederik Kingma · Shakir Mohamed · Danilo Jimenez Rezende · Max Welling -
2013 Workshop: Probabilistic Models for Big Data »
Neil D Lawrence · Joaquin Quiñonero Candela · Tianshi Gao · James Hensman · Zoubin Ghahramani · Max Welling · David Blei · Ralf Herbrich -
2012 Poster: The Time-Marginalized Coalescent Prior for Hierarchical Clustering »
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2011 Poster: Statistical Tests for Optimization Efficiency »
Levi Boyles · Anoop Korattikara · Deva Ramanan · Max Welling -
2010 Poster: On Herding and the Perceptron Cycling Theorem »
Andrew E Gelfand · Yutian Chen · Laurens van der Maaten · Max Welling -
2010 Poster: Deterministic Single-Pass Algorithm for LDA »
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2008 Session: Oral session 10: Nonparametric Processes, Scene Processing and Image Statistics »
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2008 Poster: Asynchronous Distributed Learning of Topic Models »
Arthur Asuncion · Padhraic Smyth · Max Welling -
2007 Spotlight: Collapsed Variational Inference for HDP »
Yee Whye Teh · Kenichi Kurihara · Max Welling -
2007 Spotlight: Distributed Inference for Latent Dirichlet Allocation »
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2007 Poster: Infinite State Bayes-Nets for Structured Domains »
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2007 Poster: Collapsed Variational Inference for HDP »
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2007 Poster: Distributed Inference for Latent Dirichlet Allocation »
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2007 Spotlight: Infinite State Bayes-Nets for Structured Domains »
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2006 Poster: Structure Learning in Markov Random Fields »
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2006 Spotlight: Accelerated Variational Dirichlet Process Mixtures »
Kenichi Kurihara · Max Welling · Nikos Vlassis -
2006 Poster: A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation »
Yee Whye Teh · David Newman · Max Welling