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Panel disucssion
Max Welling · Tim Genewein · Edwin Park · Song Han
Fri Dec 07 01:45 PM -- 02:45 PM (PST) @
Author Information
Max Welling (University of Amsterdam / Qualcomm AI Research)
Tim Genewein (DeepMind)
Edwin Park (Qualcomm)
Song Han (MIT)
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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 : 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 »
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2021 Poster: Memory-efficient Patch-based Inference for Tiny Deep Learning »
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2021 Poster: Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions »
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2021 : Unsupervised Indoor Wi-Fi Positioning »
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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 Poster: Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning »
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2021 Oral: E(n) Equivariant Normalizing Flows »
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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: MCUNet: Tiny Deep Learning on IoT Devices »
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2020 Spotlight: MCUNet: Tiny Deep Learning on IoT Devices »
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2020 Poster: SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks »
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2020 Poster: Differentiable Augmentation for Data-Efficient GAN Training »
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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: TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning »
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2020 Poster: The Convolution Exponential and Generalized Sylvester Flows »
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2020 Poster: Meta-trained agents implement Bayes-optimal agents »
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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 Spotlight: Meta-trained agents implement Bayes-optimal agents »
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2020 Poster: MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning »
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2019 : Hardware-aware Neural Architecture Design for Small and Fast Models: from 2D to 3D »
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2019 : TBD »
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2019 : Keynote - ML »
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2019 Workshop: Bayesian Deep Learning »
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2019 Poster: Park: An Open Platform for Learning-Augmented Computer Systems »
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2019 Poster: Deep Leakage from Gradients »
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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 »
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2019 Poster: 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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2019 Poster: Point-Voxel CNN for Efficient 3D Deep Learning »
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2019 Spotlight: Point-Voxel CNN for Efficient 3D Deep Learning »
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2018 : Making the Case for using more Inductive Bias in Deep Learning »
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2018 : Prof. Song Han »
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2018 : TBC 12 »
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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 : Neural network compression in the wild: why aiming for high compression factors is not enough »
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2018 : Bandwidth efficient deep learning by model compression »
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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 : Panel Session »
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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 »
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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 Workshop: Advances in Approximate Bayesian Inference »
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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 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 »
Anoop Korattikara Balan · Vivek Rathod · Kevin Murphy · Max Welling -
2015 Poster: Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference »
Ted Meeds · Max Welling -
2015 Poster: Variational Dropout and the Local Reparameterization Trick »
Diederik Kingma · Tim Salimans · Max Welling -
2014 Workshop: ABC in Montreal »
Max Welling · Neil D Lawrence · Richard D Wilkinson · Ted Meeds · Christian X Robert -
2014 Poster: Semi-supervised Learning with Deep Generative Models »
Diederik Kingma · Shakir Mohamed · Danilo Jimenez Rezende · Max Welling -
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 »
Levi Boyles · Max Welling -
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 »
Max Welling -
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 »
David Newman · Arthur Asuncion · Padhraic Smyth · Max Welling -
2007 Poster: Infinite State Bayes-Nets for Structured Domains »
Max Welling · Ian Porteous · Evgeniy Bart -
2007 Poster: Collapsed Variational Inference for HDP »
Yee Whye Teh · Kenichi Kurihara · Max Welling -
2007 Poster: Distributed Inference for Latent Dirichlet Allocation »
David Newman · Arthur Asuncion · Padhraic Smyth · Max Welling -
2007 Spotlight: Infinite State Bayes-Nets for Structured Domains »
Max Welling · Ian Porteous · Evgeniy Bart -
2006 Poster: Structure Learning in Markov Random Fields »
Sridevi Parise · Max Welling -
2006 Poster: Accelerated Variational Dirichlet Process Mixtures »
Kenichi Kurihara · Max Welling · Nikos Vlassis -
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