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[MISS 2016] Carsten Rother - Graphical Models in BioImedical imaging

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Variational Inference: Foundations and Innovations

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David Duvenaud: Neural Ordinary Equations

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Scott Linderman | 2019 Allen Showcase Symposium

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Recent Developments in Over-parametrized Neural Networks, Part I

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Train and Predict Materials Properties using Crystal Graph Convolutional Neural Networks (cgcnn)

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David Dunson, 'Scalable Bayes: Simple algorithms with guarantees'

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Research Sit Downs: Phil Blunsom and David Duvenaud

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Generator-aware Discriminators & Discriminator-aware Generators - David Duvenaud

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CREATING DIGITAL CHAMPIONS | diconium

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[hgraph2graph] Hierarchical Generation of Molecular Graphs using Structural Motifs | AISC Spotlight

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Recent Progress in High-Dimensional Learning

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Junyang Wang: Approximate Bayesian Solutions to Nonlinear Differential Equations

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Bob Datta: 'Inferring Internal from External State Using Motion Sequencing' at Boston Action Club

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Neural Ordinary Differential Equations with David Duvenaud - #364

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Fast Random Feature Expansions for Nonlinear Regression

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The Random Neural Network and its application to Cognitive Packet Networks - Prof. Erol Gelenbe