Frank Wood

is this you? claim profile

∙ 0 followers
University of Oxford ∙ The University of British Columbia

Associate Professor at University of Oxford, Faculty Fellow at The Alan Turing Institute, Consultant at Invrea Limited

Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective

Achieving the full promise of the Thermodynamic Variational Objective (T...
10/29/2020 ∙ by Vu Nguyen, et al. ∙ 11 ∙ share

read it
Uncertainty in Neural Processes

We explore the effects of architecture and training objective choice on ...
10/08/2020 ∙ by Saeid Naderiparizi, et al. ∙ 7 ∙ share

read it
Assisting the Adversary to Improve GAN Training

We propose a method for improved training of generative adversarial netw...
10/03/2020 ∙ by Andreas Munk, et al. ∙ 2 ∙ share

read it
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference

The recently proposed Thermodynamic Variational Objective (TVO) leverage...
07/01/2020 ∙ by Rob Brekelmans, et al. ∙ 9 ∙ share

read it
Semi-supervised Sequential Generative Models

We introduce a novel objective for training deep generative time-series ...
06/30/2020 ∙ by Michael Teng, et al. ∙ 10 ∙ share

read it
Improving Few-Shot Visual Classification with Unlabelled Examples

We propose a transductive meta-learning method that uses unlabelled inst...
06/17/2020 ∙ by Peyman Bateni, et al. ∙ 0 ∙ share

read it
Planning as Inference in Epidemiological Models

In this work we demonstrate how existing software tools can be used to a...
03/30/2020 ∙ by Frank Wood, et al. ∙ 34 ∙ share

read it
Coping With Simulators That Don't Always Return

Deterministic models are approximations of reality that are easy to inte...
03/28/2020 ∙ by Andrew Warrington, et al. ∙ 6 ∙ share

read it
Improved Few-Shot Visual Classification

Few-shot learning is a fundamental task in computer vision that carries ...
12/07/2019 ∙ by Peyman Bateni, et al. ∙ 1 ∙ share

read it
Attention for Inference Compilation

We present a new approach to automatic amortized inference in universal ...
10/25/2019 ∙ by William Harvey, et al. ∙ 21 ∙ share

read it
Deep Probabilistic Surrogate Networks for Universal Simulator Approximation

We present a framework for automatically structuring and training fast, ...
10/25/2019 ∙ by Andreas Munk, et al. ∙ 40 ∙ share

read it
Amortized Rejection Sampling in Universal Probabilistic Programming

Existing approaches to amortized inference in probabilistic programs wit...
10/20/2019 ∙ by Saeid Naderiparizi, et al. ∙ 11 ∙ share

read it
Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation

Imitation learning is a promising approach to end-to-end training of aut...
09/20/2019 ∙ by Renhao Wang, et al. ∙ 0 ∙ share

read it
The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging

We develop a stochastic whole-brain and body simulator of the nematode r...
07/24/2019 ∙ by Andrew Warrington, et al. ∙ 5 ∙ share

read it
Amortized Monte Carlo Integration

Current approaches to amortizing Bayesian inference focus solely on appr...
07/18/2019 ∙ by Adam Golinski, et al. ∙ 2 ∙ share

read it
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Probabilistic programming languages (PPLs) are receiving widespread atte...
07/08/2019 ∙ by Atılım Güneş Baydin, et al. ∙ 2 ∙ share

read it
The Thermodynamic Variational Objective

We introduce the thermodynamic variational objective (TVO) for learning ...
06/28/2019 ∙ by Vaden Masrani, et al. ∙ 2 ∙ share

read it
Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training

We introduce the use of Bayesian optimal experimental design techniques ...
06/13/2019 ∙ by William Harvey, et al. ∙ 0 ∙ share

read it
Imitation Learning of Factored Multi-agent Reactive Models

We apply recent advances in deep generative modeling to the task of imit...
03/12/2019 ∙ by Michael Teng, et al. ∙ 11 ∙ share

read it
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models

We develop a new Low-level, First-order Probabilistic Programming Langua...
03/06/2019 ∙ by Yuan Zhou, et al. ∙ 12 ∙ share

read it
An Introduction to Probabilistic Programming

This document is designed to be a first-year graduate-level introduction...
09/27/2018 ∙ by Jan-Willem van de Meent, et al. ∙ 2 ∙ share

read it
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

We present a novel framework that enables efficient probabilistic infere...
07/20/2018 ∙ by Atilim Gunes Baydin, et al. ∙ 4 ∙ share

read it
Inference Trees: Adaptive Inference with Exploration

We introduce inference trees (ITs), a new class of inference methods tha...
06/25/2018 ∙ by Tom Rainforth, et al. ∙ 2 ∙ share

read it
Deep Variational Reinforcement Learning for POMDPs

Many real-world sequential decision making problems are partially observ...
06/06/2018 ∙ by Maximilian Igl, et al. ∙ 2 ∙ share

read it
Revisiting Reweighted Wake-Sleep

Discrete latent-variable models, while applicable in a variety of settin...
05/26/2018 ∙ by Tuan Anh Le, et al. ∙ 0 ∙ share

read it
Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs

Hamiltonian Monte Carlo (HMC) is the dominant statistical inference algo...
04/07/2018 ∙ by Bradley Gram-Hansen, et al. ∙ 0 ∙ share

read it
High Throughput Synchronous Distributed Stochastic Gradient Descent

We introduce a new, high-throughput, synchronous, distributed, data-para...
03/12/2018 ∙ by Michael Teng, et al. ∙ 0 ∙ share

read it
Tighter Variational Bounds are Not Necessarily Better

We provide theoretical and empirical evidence that using tighter evidenc...
02/13/2018 ∙ by Tom Rainforth, et al. ∙ 0 ∙ share

read it
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators

We consider the problem of Bayesian inference in the family of probabili...
12/21/2017 ∙ by Mario Lezcano Casado, et al. ∙ 0 ∙ share

read it
Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference

In learning deep generative models, the encoder for variational inferenc...
12/01/2017 ∙ by Stefan Webb, et al. ∙ 0 ∙ share

read it
Updating the VESICLE-CNN Synapse Detector

We present an updated version of the VESICLE-CNN algorithm presented by ...
10/31/2017 ∙ by Andrew Warrington, et al. ∙ 0 ∙ share

read it
On the Opportunities and Pitfalls of Nesting Monte Carlo Estimators

We present a formalization of nested Monte Carlo (NMC) estimation, where...
09/18/2017 ∙ by Tom Rainforth, et al. ∙ 0 ∙ share

read it
Bayesian Optimization for Probabilistic Programs

We present the first general purpose framework for marginal maximum a po...
07/13/2017 ∙ by Tom Rainforth, et al. ∙ 0 ∙ share

read it
Learning Disentangled Representations with Semi-Supervised Deep Generative Models

Variational autoencoders (VAEs) learn representations of data by jointly...
06/01/2017 ∙ by N. Siddharth, et al. ∙ 0 ∙ share

read it
Auto-Encoding Sequential Monte Carlo

We introduce AESMC: a method for using deep neural networks for simultan...
05/29/2017 ∙ by Tuan Anh Le, et al. ∙ 0 ∙ share

read it
Online Learning Rate Adaptation with Hypergradient Descent

We introduce a general method for improving the convergence rate of grad...
03/14/2017 ∙ by Atilim Gunes Baydin, et al. ∙ 0 ∙ share

read it
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning

We draw a formal connection between using synthetic training data to opt...
03/02/2017 ∙ by Tuan Anh Le, et al. ∙ 0 ∙ share

read it
Inducing Interpretable Representations with Variational Autoencoders

We develop a framework for incorporating structured graphical models in ...
11/22/2016 ∙ by N. Siddharth, et al. ∙ 0 ∙ share

read it
Probabilistic structure discovery in time series data

Existing methods for structure discovery in time series data construct i...
11/21/2016 ∙ by David Janz, et al. ∙ 0 ∙ share

read it
Inference Compilation and Universal Probabilistic Programming

We introduce a method for using deep neural networks to amortize the cos...
10/31/2016 ∙ by Tuan Anh Le, et al. ∙ 0 ∙ share

read it
Spreadsheet Probabilistic Programming

Spreadsheet workbook contents are simple programs. Because of this, prob...
06/14/2016 ∙ by Mike Wu, et al. ∙ 0 ∙ share

read it
Inference Networks for Sequential Monte Carlo in Graphical Models

We introduce a new approach for amortizing inference in directed graphic...
02/22/2016 ∙ by Brooks Paige, et al. ∙ 0 ∙ share

read it
Interacting Particle Markov Chain Monte Carlo

We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a P...
02/16/2016 ∙ by Tom Rainforth, et al. ∙ 0 ∙ share

read it
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints

We study the semantic foundation of expressive probabilistic programming...
01/19/2016 ∙ by Sam Staton, et al. ∙ 0 ∙ share

read it
Data-driven Sequential Monte Carlo in Probabilistic Programming

Most of Markov Chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC)...
12/14/2015 ∙ by Yura N Perov, et al. ∙ 0 ∙ share

read it
A New Approach to Probabilistic Programming Inference

We introduce and demonstrate a new approach to inference in expressive p...
07/03/2015 ∙ by Frank Wood, et al. ∙ 0 ∙ share

read it
Maximum a Posteriori Estimation by Search in Probabilistic Programs

We introduce an approximate search algorithm for fast maximum a posterio...
04/26/2015 ∙ by David Tolpin, et al. ∙ 0 ∙ share

read it
Path Finding under Uncertainty through Probabilistic Inference

We introduce a new approach to solving path-finding problems under uncer...
02/25/2015 ∙ by David Tolpin, et al. ∙ 0 ∙ share

read it
Particle Gibbs with Ancestor Sampling for Probabilistic Programs

Particle Markov chain Monte Carlo techniques rank among current state-of...
01/27/2015 ∙ by Jan-Willem van de Meent, et al. ∙ 0 ∙ share

read it
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs

We introduce an adaptive output-sensitive Metropolis-Hastings algorithm ...
01/22/2015 ∙ by David Tolpin, et al. ∙ 0 ∙ share

read it

Frank Wood

Featured Co-authors