208 citations · 963 across the 30 of their papers we have counts for
8 papers · 1 filter
I Don't Need u: Identifiable Non-Linear ICA Without Side Information
Matthew Willetts, Brooks Paige
In this paper, we investigate the algorithmic stability of unsupervised representation learning with deep generative models, as a function of repeated re-training on the same input…
Barking up the right tree: an approach to search over molecule synthesis DAGs
John Bradshaw, Brooks Paige, Matt J. Kusner +2
When designing new molecules with particular properties, it is not only important what to make but crucially how to make it. These instructions form a synthesis directed acyclic gr…
Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning
Aneesh Pappu, Brooks Paige
Graph neural networks have become very popular for machine learning on molecules due to the expressive power of their learnt representations. However, molecular machine learning is…
Goal-directed Generation of Discrete Structures with Conditional Generative Models
Amina Mollaysa, Brooks Paige, Alexandros Kalousis
Despite recent advances, goal-directed generation of structured discrete data remains challenging. For problems such as program synthesis (generating source code) and materials des…
Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models
Yuge Shi, Brooks Paige, Philip H. S. Torr +1
Multimodal learning for generative models often refers to the learning of abstract concepts from the commonality of information in multiple modalities, such as vision and language.…
Learning Bijective Feature Maps for Linear ICA
Alexander Camuto, Matthew Willetts, Brooks Paige +2
Separating high-dimensional data like images into independent latent factors, i.e independent component analysis (ICA), remains an open research problem. As we show, existing proba…
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…