208 citations · 963 across the 30 of their papers we have counts for
33 papers
Moment Matching Denoising Gibbs Sampling
Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige +1
Energy-Based Models (EBMs) offer a versatile framework for modeling complex data distributions. However, training and sampling from EBMs continue to pose significant challenges. Th…
Towards Healing the Blindness of Score Matching
Mingtian Zhang, Oscar Key, Peter Hayes +3
Score-based divergences have been widely used in machine learning and statistics applications. Despite their empirical success, a blindness problem has been observed when using the…
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…
Simulation Intelligence: Towards a New Generation of Scientific Methods
Alexander Lavin, David Krakauer, Hector Zenil +21
The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of comput…
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes
Hugh Dance, Brooks Paige
Variable selection in Gaussian processes (GPs) is typically undertaken by thresholding the inverse lengthscales of automatic relevance determination kernels, but in high-dimensiona…
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…
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.…
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…
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…
An Introduction to Probabilistic Programming
Jan-Willem van de Meent, Brooks Paige, Hongseok Yang +1
This book is a graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, bu…
Take a Look Around: Using Street View and Satellite Images to Estimate House Prices
Stephen Law, Brooks Paige, Chris Russell
When an individual purchases a home, they simultaneously purchase its structural features, its accessibility to work, and the neighborhood amenities. Some amenities, such as air qu…
A Generative Model For Electron Paths
John Bradshaw, Matt J. Kusner, Brooks Paige +2
Chemical reactions can be described as the stepwise redistribution of electrons in molecules. As such, reactions are often depicted using `arrow-pushing' diagrams which show this m…
Structured Disentangled Representations
Babak Esmaeili, Hao Wu, Sarthak Jain +6
Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner. A number of recent efforts have focused on learning representations that disen…
Learning a Generative Model for Validity in Complex Discrete Structures
David Janz, Jos van der Westhuizen, Brooks Paige +2
Deep generative models have been successfully used to learn representations for high-dimensional discrete spaces by representing discrete objects as sequences and employing powerfu…
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
N. Siddharth, Brooks Paige, Jan-Willem van de Meent +5
Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the dat…
Grammar Variational Autoencoder
Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato
Deep generative models have been wildly successful at learning coherent latent representations for continuous data such as video and audio. However, generative modeling of discrete…
Inducing Interpretable Representations with Variational Autoencoders
N. Siddharth, Brooks Paige, Alban Desmaison +5
We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representation…
Probabilistic structure discovery in time series data
David Janz, Brooks Paige, Tom Rainforth +2
Existing methods for structure discovery in time series data construct interpretable, compositional kernels for Gaussian process regression models. While the learned Gaussian proce…