Search Results for author: Alexander L. Gaunt

Found 12 papers, 8 papers with code

Deterministic Variational Inference for Robust Bayesian Neural Networks

3 code implementations ICLR 2019 Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt

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.

Variational Inference

Generative Code Modeling with Graphs

1 code implementation ICLR 2019 Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov

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.

Structured Prediction

AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks

1 code implementation ICLR 2018 Alexander L. Gaunt, Matthew A. Johnson, Maik Riechert, Daniel Tarlow, Ryota Tomioka, Dimitrios Vytiniotis, Sam Webster

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.

DeepCoder: Learning to Write Programs

3 code implementations7 Nov 2016 Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow

We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning.

Enumerative Search

Differentiable Programs with Neural Libraries

no code implementations ICML 2017 Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow

We develop a framework for combining differentiable programming languages with neural networks.

Differentiable Functional Program Interpreters

1 code implementation7 Nov 2016 John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow

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.

Program Synthesis

TerpreT: A Probabilistic Programming Language for Program Induction

no code implementations15 Aug 2016 Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow

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).

BIG-bench Machine Learning Probabilistic Programming +2

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