140 citations · 380 across the 9 of their papers we have counts for
16 papers
Improving VAE-based Representation Learning
Mingtian Zhang, Tim Z. Xiao, Brooks Paige +1
Latent variable models like the Variational Auto-Encoder (VAE) are commonly used to learn representations of images. However, for downstream tasks like semantic classification, the…
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…
Bayesian Graph Neural Networks for Molecular Property Prediction
George Lamb, Brooks Paige
Graph neural networks for molecular property prediction are frequently underspecified by data and fail to generalise to new scaffolds at test time. A potential solution is Bayesian…
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.…