David Duvenaud - A farewell to GPs: A roadmap for ultra-scalable simulation and inference in SPDEs

(1:36)

Research Sit Downs: Phil Blunsom and David Duvenaud

(16:49)

AI Risk (Robin Hanson \u0026 Agnes Callard, with David Duvenaud)

(1:8:7)

MIA Special Seminar: David Duvenaud, It's time to talk about irregularly-sampled time series

(54:43)

David Duvenaud: Neural Ordinary Equations

(30:51)

Neural Ordinary Differential Equations with David Duvenaud - #364

(48:54)

David Duvenaud: When should we make our models continuous in time?

(1:2:46)

David Duvenaud | Explaining decisions by generating counterfactuals

(1:19:44)

David Duvenaud - Handling messy time series with large latent-variable models

(1:1:31)

Composing Graphical Models With Neural Networks, w/ David Duvenaud - #96

(37:32)

Latent Stochastic Differential Equations for Irregularly-Sampled Time Series - David Duvenaud

(1:5:28)

CAIDA Talk - Dec 6, 2019 - David Duvenaud

(58:49)

David Duvenaud: Self-tuning Gradient Estimators for Discrete Random Variables

(35:36)

Generator-aware Discriminators \u0026 Discriminator-aware Generators - David Duvenaud

(25:35)

David Duvenaud – No more mini-languages: The power of autodiffing full-featured Python

(25:54)

David Duvenaud | Reflecting on Neural ODEs | NeurIPS 2019

(21:2)

E2: University of Toronto's David Duvenaud on the Future of AI \u0026 Academia

(37:29)

Fast Random Feature Expansions for Nonlinear Regression

(1:30:22)

Yeshua | Jesus Image | Michael Koulianos

(19:8)

The Godfather in Conversation: Why Geoffrey Hinton is worried about the future of AI

(46:21)

The Brains Behind AI: Hugo Larochelle

(1:54)

TMLS2019 - David Duvenaud

(24:47)