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Making the Case for using more Inductive Bias in Deep Learning
Max Welling
Author Information
Max Welling (University of Amsterdam / Qualcomm AI Research)
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2021 Workshop: Bayesian Deep Learning »
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2021 Poster: Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions »
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2021 Poster: Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent »
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2021 Oral: E(n) Equivariant Normalizing Flows »
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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: Experimental design for MRI by greedy policy search »
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2019 : TBD »
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2019 : Keynote - ML »
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2019 Poster: Integer Discrete Flows and Lossless Compression »
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2019 Poster: Combining Generative and Discriminative Models for Hybrid Inference »
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2017 : Panel Session »
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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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2014 Workshop: ABC in Montreal »
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2010 Poster: On Herding and the Perceptron Cycling Theorem »
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