← Authors

David K. Duvenaud

17 papers · 2011–2023 · 1 conference · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (15) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (33) πŸƒ Academic Marathon (12) 🌈 Renaissance Researcher (6) 🌟 Keyword Trendsetter Combo (5) πŸ† Keyword Champion πŸ”₯ Unstoppable (8) πŸ“ˆ Trend Setter πŸ’Ž Century Club (17) πŸ—ƒοΈ Keyword Collector (83) ❓ The Questioner

Conferences

NIPS (17)

Top co-authors

Ricky T. Q. Chen (5) Jesse Bettencourt (2) Matthew J Johnson (2) Ryan P. Adams (2) Carl E. Rasmussen (2) Yulia Rubanova (2) Geoffrey Roeder (1) Eric Schulz (1) Michael Schober (1) Raquel Urtasun (1)

Keywords

neural network (3) ordinary differential equation (3) bayesian inference (2) graph neural network (2) variational autoencoder (2) variational inference (2) gaussian process (2) uncertainty quantification (2) density estimation (1) attention mechanism (1) representation learning (1) kl divergence (1) data provenance (1) graph generation (1) regression (1) self-supervised learning (1) marginal likelihood (1) automatic differentiation (1) hyperparameter optimization (1) kernel learning (1)

Papers

Tools for Verifying Neural Models' Training Data NIPS 2023
Meta-learning to Improve Pre-training NIPS 2021
What went wrong and when? Instance-wise feature importance for time-series black-box models NIPS 2020
Learning Differential Equations that are Easy to Solve NIPS 2020
Residual Flows for Invertible Generative Modeling NIPS 2019
Efficient Graph Generation with Graph Recurrent Attention Networks NIPS 2019
Neural Networks with Cheap Differential Operators NIPS 2019
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series NIPS 2019
Neural Ordinary Differential Equations NIPS 2018
Isolating Sources of Disentanglement in Variational Autoencoders NIPS 2018
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference NIPS 2017
Composing graphical models with neural networks for structured representations and fast inference NIPS 2016
Probing the Compositionality of Intuitive Functions NIPS 2016
Convolutional Networks on Graphs for Learning Molecular Fingerprints NIPS 2015
Probabilistic ODE Solvers with Runge-Kutta Means NIPS 2014
Active Learning of Model Evidence Using Bayesian Quadrature NIPS 2012
Additive Gaussian Processes NIPS 2011