David Duvenaud Neural Ordinary Equations

May 26, 2026
Media Summary: Presentation slides can be found here: Check ... EECS Colloquium Wednesday, October 5, 2022 306 Soda Hall (HP Auditorium) 4-5p Caption available upon request. Seminar on Theoretical Machine Learning Topic: Latent Stochastic

David Duvenaud Neural Ordinary Equations - Detailed Analysis & Overview

Presentation slides can be found here: Check ... EECS Colloquium Wednesday, October 5, 2022 306 Soda Hall (HP Auditorium) 4-5p Caption available upon request. Seminar on Theoretical Machine Learning Topic: Latent Stochastic Our weekly SRI Seminar Series welcomes Schwartz Reisman Institute for Technology and Society Chair Abstract: We show how to do gradient-based stochastic variational inference in stochastic A talk from the Toronto Machine Learning Summit: The video is hosted by ...

UBC CAIDA hosts an AI seminar, given by Dr.

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David Duvenaud: When should we make our models continuous in time?
Neural ODEs (NODEs) [Physics Informed Machine Learning]
Neural Ordinary Differential Equations with David Duvenaud - #364
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David Duvenaud | Reflecting on Neural ODEs | NeurIPS 2019
David Duvenaud – No more mini-languages: The power of autodiffing full-featured Python
Latent Stochastic Differential Equations | David Duvenaud

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David Duvenaud: Neural Ordinary Equations

David Duvenaud: Neural Ordinary Equations

Presentation slides can be found here: https://vectorinstitute.ai/wp-content/uploads/2019/03/ode-talk-vector-symposium.pdf Check ...

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

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

EECS Colloquium Wednesday, October 5, 2022 306 Soda Hall (HP Auditorium) 4-5p Caption available upon request.

Neural ODEs (NODEs) [Physics Informed Machine Learning]

Neural ODEs (NODEs) [Physics Informed Machine Learning]

This video describes

Neural Ordinary Differential Equations with David Duvenaud - #364

Neural Ordinary Differential Equations with David Duvenaud - #364

Today we're joined by

ODE | Neural Ordinary Differential Equations - Best Paper Awards NeurIPS

ODE | Neural Ordinary Differential Equations - Best Paper Awards NeurIPS

Neural Ordinary Differential Equations

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

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

Seminar on Theoretical Machine Learning Topic: Latent Stochastic

David Duvenaud | The big picture of LLM dangerous capability evals

David Duvenaud | The big picture of LLM dangerous capability evals

Our weekly SRI Seminar Series welcomes Schwartz Reisman Institute for Technology and Society Chair

David Duvenaud - Latent Stochastic Differential Equations: An Unexplored Model Class

David Duvenaud - Latent Stochastic Differential Equations: An Unexplored Model Class

Abstract: We show how to do gradient-based stochastic variational inference in stochastic

David Duvenaud | Explaining decisions by generating counterfactuals

David Duvenaud | Explaining decisions by generating counterfactuals

Our weekly SRI Seminar Series welcomes

David Duvenaud | Reflecting on Neural ODEs | NeurIPS 2019

David Duvenaud | Reflecting on Neural ODEs | NeurIPS 2019

Original paper: https://arxiv.org/abs/1806.07366

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

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

[NIPS 2016] Autodiff Workshop Speaker:

Latent Stochastic Differential Equations | David Duvenaud

Latent Stochastic Differential Equations | David Duvenaud

A talk from the Toronto Machine Learning Summit: https://torontomachinelearning.com/ The video is hosted by ...

CAIDA Talk - Dec 6, 2019 - David Duvenaud

CAIDA Talk - Dec 6, 2019 - David Duvenaud

UBC CAIDA hosts an AI seminar, given by Dr.