Search Results for author: Luke Hewitt

Found 5 papers, 2 papers with code

Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface

no code implementations ICLR 2022 Tuan Anh Le, Katherine M. Collins, Luke Hewitt, Kevin Ellis, N. Siddharth, Samuel J. Gershman, Joshua B. Tenenbaum

We build on a recent approach, Memoised Wake-Sleep (MWS), which alleviates part of the problem by memoising discrete variables, and extend it to allow for a principled and effective way to handle continuous variables by learning a separate recognition model used for importance-sampling based approximate inference and marginalization.

Scene Understanding Time Series +1

DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning

3 code implementations15 Jun 2020 Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Joshua B. Tenenbaum

It builds expertise by creating programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages.

Drawing Pictures Program induction +1

Learning to Infer Program Sketches

1 code implementation17 Feb 2019 Maxwell Nye, Luke Hewitt, Joshua Tenenbaum, Armando Solar-Lezama

Our goal is to build systems which write code automatically from the kinds of specifications humans can most easily provide, such as examples and natural language instruction.

Memorization Program Synthesis

The Variational Homoencoder: Learning to Infer High-Capacity Generative Models from Few Examples

no code implementations ICLR 2018 Luke Hewitt, Andrea Gane, Tommi Jaakkola, Joshua B. Tenenbaum

Hierarchical Bayesian methods have the potential to unify many related tasks (e. g. k-shot classification, conditional, and unconditional generation) by framing each as inference within a single generative model.

General Classification Test

Cannot find the paper you are looking for? You can Submit a new open access paper.