Search Results for author:
Found 12 papers, 8 papers with code
Learned Force Fields Are Ready For Ground State Catalyst Discovery
We present evidence that learned density functional theory (``DFT'') force fields are ready for ground state catalyst discovery.
Learning to Represent Edits
We introduce the problem of learning distributed representations of edits.
Deterministic Variational Inference for Robust Bayesian Neural Networks
We provide two innovations that aim to turn VB into a robust inference tool for Bayesian neural networks: first, 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.
Constrained Graph Variational Autoencoders for Molecule Design
Graphs are ubiquitous data structures for representing interactions between entities.
Generative Code Modeling with Graphs
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.
Graph Partition Neural Networks for Semi-Supervised Classification
We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs.
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
Through an implementation on multi-core CPUs, we show that AMP training converges to the same accuracy as conventional synchronous training algorithms in a similar number of epochs, but utilizes the available hardware more efficiently even for small minibatch sizes, resulting in significantly shorter overall training times.
Summary - TerpreT: A Probabilistic Programming Language for Program Induction
A TerpreT model is composed of a specification of a program representation and an interpreter that describes how programs map inputs to outputs.
Differentiable Programs with Neural Libraries
We develop a framework for combining differentiable programming languages with neural networks.
Differentiable Functional Program Interpreters
Recent work on differentiable interpreters relaxes the discrete space of programs into a continuous space so that search over programs can be performed using gradient-based optimization.
DeepCoder: Learning to Write Programs
We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning.
TerpreT: A Probabilistic Programming Language for Program Induction
TerpreT is similar to a probabilistic programming language: a model is composed of a specification of a program representation (declarations of random variables) and an interpreter describing how programs map inputs to outputs (a model connecting unknowns to observations).
