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Invited talk 1: Deep recurrent inverse modeling for radio astronomy and fast MRI imaging
Max Welling
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Max Welling (University of Amsterdam / Qualcomm AI Research)
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2021 : Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data »
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2021 : Particle Dynamics for Learning EBMs »
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2022 : PIPS: Path Integral Stochastic Optimal Control for Path Sampling in Molecular Dynamics »
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2022 : Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design »
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2022 : Program Synthesis for Integer Sequence Generation »
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2022 : Structure-based Drug Design with Equivariant Diffusion Models »
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2022 Spotlight: Alleviating Adversarial Attacks on Variational Autoencoders with MCMC »
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2022 : Invited Speaker »
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2022 : Invited Talk #4, The Fifth Paradigm of Scientific Discovery, Max Welling »
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2022 Workshop: AI for Science: Progress and Promises »
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2022 Poster: Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel »
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2022 Poster: Alleviating Adversarial Attacks on Variational Autoencoders with MCMC »
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2022 Poster: On the symmetries of the synchronization problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane »
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2021 : Particle Dynamics for Learning EBMs »
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2021 : General Discussion 1 - What is out of distribution (OOD) generalization and why is it important? with Yoshua Bengio, Leyla Isik, Max Welling »
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2021 Workshop: Bayesian Deep Learning »
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2021 : Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders »
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2021 : Live Panel »
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2021 : Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders »
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2021 : Session 1 | Invited talk: Max Welling, "Accelerating simulations of nature, both classical and quantum, with equivariant 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 -
2021 Poster: Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions »
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2021 Poster: Topographic VAEs learn Equivariant Capsules »
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2021 : Unsupervised Indoor Wi-Fi Positioning »
Farhad G. Zanjani · Ilia Karmanov · Hanno Ackermann · Daniel Dijkman · Max Welling · Ishaque Kadampot · Simone Merlin · Steve Shellhammer · Rui Liang · Brian Buesker · Harshit Joshi · Vamsi Vegunta · Raamkumar Balamurthi · Bibhu Mohanty · Joseph Soriaga · Ron Tindall · Pat Lawlor -
2021 Poster: Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent »
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2021 Poster: E(n) Equivariant Normalizing Flows »
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2021 Poster: Modality-Agnostic Topology Aware Localization »
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2021 Oral: E(n) Equivariant Normalizing Flows »
Victor Garcia Satorras · Emiel Hoogeboom · Fabian Fuchs · Ingmar Posner · Max Welling -
2020 : Invited Talk: Max Welling - The LIAR (Learning with Interval Arithmetic Regularization) is Dead »
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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 »
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2020 Poster: Experimental design for MRI by greedy policy search »
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2020 Spotlight: Experimental design for MRI by greedy policy search »
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2020 Poster: MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning »
Elise van der Pol · Daniel E Worrall · Herke van Hoof · Frans Oliehoek · Max Welling -
2019 : TBD »
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2019 : Keynote - ML »
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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 »
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2019 Poster: Integer Discrete Flows and Lossless Compression »
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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 : Making the Case for using more Inductive Bias in Deep Learning »
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2018 : Panel disucssion »
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2018 : Efficient Computation of Deep Convolutional Neural Networks: A Quantization Perspective »
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2018 : Prof. Max Welling »
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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 »
Lixin Fan · Zhouchen Lin · Max Welling · Yurong Chen · Werner Bailer -
2018 Poster: Graphical Generative Adversarial Networks »
Chongxuan LI · Max Welling · Jun Zhu · Bo Zhang -
2018 Poster: 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data »
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2017 : Panel Session »
Neil Lawrence · Finale Doshi-Velez · Zoubin Ghahramani · Yann LeCun · Max Welling · Yee Whye Teh · Ole Winther -
2017 : Deep Bayes for Distributed Learning, Uncertainty Quantification and Compression »
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2017 Workshop: Bayesian Deep Learning »
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2017 : Panel session »
Iain Murray · Max Welling · Juan Carrasquilla · Anatole von Lilienfeld · Gilles Louppe · Kyle Cranmer -
2017 : Panel: On the Foundations and Future of Approximate Inference »
David Blei · Zoubin Ghahramani · Katherine Heller · Tim Salimans · Max Welling · Matthew D. Hoffman -
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 »
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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 Workshop: Bayesian Deep Learning »
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2016 Poster: Improving Variational Autoencoders with Inverse Autoregressive Flow »
Diederik Kingma · Tim Salimans · Rafal Jozefowicz · Peter Chen · Xi Chen · Ilya Sutskever · Max Welling -
2015 Workshop: Scalable Monte Carlo Methods for Bayesian Analysis of Big Data »
Babak Shahbaba · Yee Whye Teh · Max Welling · Arnaud Doucet · Christophe Andrieu · Sebastian J. Vollmer · Pierre Jacob -
2015 : *Max Welling* Optimization Monte Carlo »
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2015 Symposium: Deep Learning Symposium »
Yoshua Bengio · Marc'Aurelio Ranzato · Honglak Lee · Max Welling · Andrew Y Ng -
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 »
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2014 Spotlight: Semi-supervised Learning with Deep Generative Models »
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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 -
2008 Session: Oral session 10: Nonparametric Processes, Scene Processing and Image Statistics »
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2008 Poster: Asynchronous Distributed Learning of Topic Models »
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2007 Spotlight: Collapsed Variational Inference for HDP »
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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 »
Yee Whye Teh · Kenichi Kurihara · Max Welling -
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 Poster: Accelerated Variational Dirichlet Process Mixtures »
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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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