140 citations · 392 across the 13 of their papers we have counts for
3 papers · 1 filter
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models
Yuge Shi, N. Siddharth, Brooks Paige +1
Learning generative models that span multiple data modalities, such as vision and language, is often motivated by the desire to learn more useful, generalisable representations tha…
Data Generation for Neural Programming by Example
Judith Clymo, Haik Manukian, Nathanaël Fijalkow +2
Programming by example is the problem of synthesizing a program from a small set of input / output pairs. Recent works applying machine learning methods to this task show promise,…
A Model to Search for Synthesizable Molecules
John Bradshaw, Brooks Paige, Matt J. Kusner +2
Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models allow one to generate mole…