• Publications
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DeepCoder: Learning to Write Programs
TLDR
We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning. Expand
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Constrained Graph Variational Autoencoders for Molecule Design
TLDR
We propose a novel probabilistic model for graph generation that builds gated graph neural networks into the encoder and decoder of a variational autoencoder. Expand
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Generative Code Modeling with Graphs
TLDR
Generative models for source code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs. Expand
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Deterministic Variational Inference for Robust Bayesian Neural Networks
TLDR
We introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel Empirical Bayes procedure for automatically selecting prior variances. Expand
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Bose-Einstein condensation of atoms in a uniform potential.
We have observed the Bose-Einstein condensation of an atomic gas in the (quasi)uniform three-dimensional potential of an optical box trap. Condensation is seen in the bimodal momentum distributionExpand
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TerpreT: A Probabilistic Programming Language for Program Induction
TLDR
We study machine learning formulations of inductive program synthesis; given input-output examples, we try to synthesize source code that maps inputs to corresponding outputs. Expand
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Differentiable Programs with Neural Libraries
TLDR
We develop a framework for combining differentiable programming languages with neural networks to create end-to-end trainable systems that learn to write interpretable algorithms with perceptual components. Expand
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Robust Digital Holography For Ultracold Atom Trapping
TLDR
We have formulated and experimentally demonstrated an improved algorithm for design of arbitrary two-dimensional holographic traps for ultracold atoms. Expand
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Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks
TLDR
We introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel empirical Bayes procedure for automatically selecting prior variances. Expand
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Critical dynamics of spontaneous symmetry breaking in a homogeneous Bose gas
Breaking the symmetry in an atomic gas Cooling a physical system through a phase transition typically makes it less symmetrical. If the cooling is done very slowly, this symmetry change is uniformExpand
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