D. Duvenaud

Publications88
h-index42
Citations13,738
Highly Influential Citations1,868
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Neural Ordinary Differential Equations
TLDR
This work shows how to scalably backpropagate through any ODE solver, without access to its internal operations, which allows end-to-end training of ODEs within larger models.
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Isolating Sources of Disentanglement in Variational Autoencoders
We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables. We use this to motivate our $\beta$-TCVAE (Total Correlation…
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Convolutional Networks on Graphs for Learning Molecular Fingerprints
TLDR
A convolutional neural network that operates directly on graphs that allows end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape is introduced.
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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration…
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FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
TLDR
This paper uses Hutchinson's trace estimator to give a scalable unbiased estimate of the log-density and demonstrates the approach on high-dimensional density estimation, image generation, and variational inference, achieving the state-of-the-art among exact likelihood methods with efficient sampling.
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Gradient-based Hyperparameter Optimization through Reversible Learning
TLDR
This work computes exact gradients of cross-validation performance with respect to all hyperparameters by chaining derivatives backwards through the entire training procedure, which allows us to optimize thousands ofhyperparameters, including step-size and momentum schedules, weight initialization distributions, richly parameterized regularization schemes, and neural network architectures.
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Invertible Residual Networks
TLDR
The empirical evaluation shows that invertible ResNets perform competitively with both state-of-the-art image classifiers and flow-based generative models, something that has not been previously achieved with a single architecture.
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Structure Discovery in Nonparametric Regression through Compositional Kernel Search
TLDR
This work defines a space of kernel structures which are built compositionally by adding and multiplying a small number of base kernels, and presents a method for searching over this space of structures which mirrors the scientific discovery process.
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Backpropagation through the Void: Optimizing control variates for black-box gradient estimation
TLDR
This work introduces a general framework for learning low-variance, unbiased gradient estimators for black-box functions of random variables, and gives an unbiased, action-conditional extension of the advantage actor-critic reinforcement learning algorithm.
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Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
TLDR
This work generalizes RNNs to have continuous-time hidden dynamics defined by ordinary differential equations (ODEs), a model they are called ODE-RNNs, which outperform their RNN-based counterparts on irregularly-sampled data.
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