David K. Duvenaud
17 papers · 2011–2023 · 1 venue · across top CS/AI venues
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Achievements
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π£ Hot Topic Early Bird πΊοΈ Taxonomy Completionist (37) π Academic Marathon Β· Half π Interdisciplinary Bridge Β· Bronze π Keyword Trendsetter Combo (6)
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Keyword Pioneer
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Trend Setter
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Century Club Β· Bronze
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Renaissance Researcher
(5)
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The Questioner
ποΈ
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(91)
Venues
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Research topics
Machine Learning
(12)
Deep Learning
(10)
Optimization & Theory
(9)
Artificial Intelligence
(8)
Architectures
(8)
Bayesian & Probabilistic Β· Artificial Intelligence
(5)
Core Methods
(4)
Models
(4)
Bayesian & Probabilistic Β· Machine Learning
(3)
Learning Paradigms
(3)
Interdisciplinary
(2)
Mathematics & Optimization
(2)
Learning Types Β· Deep Learning
(2)
Learning Types Β· Machine Learning
(2)
Mathematics
(2)
Keywords
ordinary differential equation
(3)
neural network
(3)
gaussian process
(3)
bayesian inference
(2)
graph neural network
(2)
variational inference
(2)
uncertainty quantification
(2)
variational autoencoder
(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
NeurIPS 2023
Meta-learning to Improve Pre-training
NeurIPS 2021
Learning Differential Equations that are Easy to Solve
NeurIPS 2020
What went wrong and when? Instance-wise feature importance for time-series black-box models
NeurIPS 2020
Efficient Graph Generation with Graph Recurrent Attention Networks
NeurIPS 2019
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
NeurIPS 2019
Neural Networks with Cheap Differential Operators
NeurIPS 2019
Residual Flows for Invertible Generative Modeling
NeurIPS 2019
Isolating Sources of Disentanglement in Variational Autoencoders
NeurIPS 2018
Neural Ordinary Differential Equations
NeurIPS 2018
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference
NeurIPS 2017
Composing graphical models with neural networks for structured representations and fast inference
NeurIPS 2016
Probing the Compositionality of Intuitive Functions
NeurIPS 2016
Convolutional Networks on Graphs for Learning Molecular Fingerprints
NeurIPS 2015
Probabilistic ODE Solvers with Runge-Kutta Means
NeurIPS 2014
Active Learning of Model Evidence Using Bayesian Quadrature
NeurIPS 2012
Additive Gaussian Processes
NeurIPS 2011