
I Don't Need 𝐮: Identifiable NonLinear ICA Without Side Information
In this work we introduce a new approach for identifiable nonlinear ICA...
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Barking up the right tree: an approach to search over molecule synthesis DAGs
When designing new molecules with particular properties, it is not only ...
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Making Graph Neural Networks Worth It for LowData Molecular Machine Learning
Graph neural networks have become very popular for machine learning on m...
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Goaldirected Generation of Discrete Structures with Conditional Generative Models
Despite recent advances, goaldirected generation of structured discrete...
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Relating by Contrasting: A Dataefficient Framework for Multimodal Generative Models
Multimodal learning for generative models often refers to the learning o...
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Learning Bijective Feature Maps for Linear ICA
Separating highdimensional data like images into independent latent fac...
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Variational MixtureofExperts Autoencoders for MultiModal Deep Generative Models
Learning generative models that span multiple data modalities, such as v...
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Data Generation for Neural Programming by Example
Programming by example is the problem of synthesizing a program from a s...
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A Model to Search for Synthesizable Molecules
Deep generative models are able to suggest new organic molecules by gene...
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An Introduction to Probabilistic Programming
This document is designed to be a firstyear graduatelevel introduction...
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Take a Look Around: Using Street View and Satellite Images to Estimate House Prices
When an individual purchases a home, they simultaneously purchase its st...
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Predicting Electron Paths
Chemical reactions can be described as the stepwise redistribution of el...
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Hierarchical Disentangled Representations
Deep latentvariable models learn representations of highdimensional da...
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Learning a Generative Model for Validity in Complex Discrete Structures
Deep generative models have been successfully used to learn representati...
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Learning Disentangled Representations with SemiSupervised Deep Generative Models
Variational autoencoders (VAEs) learn representations of data by jointly...
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Grammar Variational Autoencoder
Deep generative models have been wildly successful at learning coherent ...
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Inducing Interpretable Representations with Variational Autoencoders
We develop a framework for incorporating structured graphical models in ...
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Probabilistic structure discovery in time series data
Existing methods for structure discovery in time series data construct i...
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Inference Networks for Sequential Monte Carlo in Graphical Models
We introduce a new approach for amortizing inference in directed graphic...
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Interacting Particle Markov Chain Monte Carlo
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a P...
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Kernel Sequential Monte Carlo
We propose kernel sequential Monte Carlo (KSMC), a framework for samplin...
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Path Finding under Uncertainty through Probabilistic Inference
We introduce a new approach to solving pathfinding problems under uncer...
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OutputSensitive Adaptive MetropolisHastings for Probabilistic Programs
We introduce an adaptive outputsensitive MetropolisHastings algorithm ...
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Asynchronous Anytime Sequential Monte Carlo
We introduce a new sequential Monte Carlo algorithm we call the particle...
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A Compilation Target for Probabilistic Programming Languages
Forward inference techniques such as sequential Monte Carlo and particle...
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Tempering by Subsampling
In this paper we demonstrate that tempering Markov chain Monte Carlo sam...
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Brooks Paige
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Research fellow at the Alan Turing Institute, and affiliated with the University of Cambridge since 2016, DPhil student in the Machine Learning Research Group at University of Oxford