102 citations · 315 across the 32 of their papers we have counts for
33 papers
A Diffusion-Model of Joint Interactive Navigation
Matthew Niedoba, Jonathan Wilder Lavington, Yunpeng Liu +8
Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of prerecorded real-world traffic scenarios i…
Don't be so negative! Score-based Generative Modeling with Oracle-assisted Guidance
Saeid Naderiparizi, Xiaoxuan Liang, Setareh Cohan +2
Score-based diffusion models are a powerful class of generative models, widely utilized across diverse domains. Despite significant advancements in large-scale tasks such as text-t…
Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models
Setareh Dabiri, Vasileios Lioutas, Berend Zwartsenberg +8
When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to the distribution of real data. We invest…
Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images
Yunpeng Liu, Vasileios Lioutas, Jonathan Wilder Lavington +8
The development of algorithms that learn multi-agent behavioral models using human demonstrations has led to increasingly realistic simulations in the field of autonomous driving.…
Visual Chain-of-Thought Diffusion Models
William Harvey, Frank Wood
Recent progress with conditional image diffusion models has been stunning, and this holds true whether we are speaking about models conditioned on a text description, a scene layou…
Graphically Structured Diffusion Models
Christian Weilbach, William Harvey, Frank Wood
We introduce a framework for automatically defining and learning deep generative models with problem-specific structure. We tackle problem domains that are more traditionally solve…
Vehicle Type Specific Waypoint Generation
Yunpeng Liu, Jonathan Wilder Lavington, Adam Scibior +1
We develop a generic mechanism for generating vehicle-type specific sequences of waypoints from a probabilistic foundation model of driving behavior. Many foundation behavior model…
Conditional Permutation Invariant Flows
Berend Zwartsenberg, Adam Ścibior, Matthew Niedoba +7
We present a novel, conditional generative probabilistic model of set-valued data with a tractable log density. This model is a continuous normalizing flow governed by permutation…
Critic Sequential Monte Carlo
Vasileios Lioutas, Jonathan Wilder Lavington, Justice Sefas +6
We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q function heuristic factors. These heuristic…
Flexible Diffusion Modeling of Long Videos
William Harvey, Saeid Naderiparizi, Vaden Masrani +2
We present a framework for video modeling based on denoising diffusion probabilistic models that produces long-duration video completions in a variety of realistic environments. We…
Gradients without Backpropagation
Atılım Güneş Baydin, Barak A. Pearlmutter, Don Syme +2
Using backpropagation to compute gradients of objective functions for optimization has remained a mainstay of machine learning. Backpropagation, or reverse-mode differentiation, is…
Exploration with Multi-Sample Target Values for Distributional Reinforcement Learning
Michael Teng, Michiel van de Panne, Frank Wood
Distributional reinforcement learning (RL) aims to learn a value-network that predicts the full distribution of the returns for a given state, often modeled via a quantile-based cr…
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning
Peyman Bateni, Jarred Barber, Raghav Goyal +4
Modern deep learning requires large-scale extensively labelled datasets for training. Few-shot learning aims to alleviate this issue by learning effectively from few labelled examp…
q-Paths: Generalizing the Geometric Annealing Path using Power Means
Vaden Masrani, Rob Brekelmans, Thang Bui +4
Many common machine learning methods involve the geometric annealing path, a sequence of intermediate densities between two distributions of interest constructed using the geometri…
Differentiable Particle Filtering without Modifying the Forward Pass
Adam Ścibior, Frank Wood
Particle filters are not compatible with automatic differentiation due to the presence of discrete resampling steps. While known estimators for the score function, based on Fisher'…
Conditional Image Generation by Conditioning Variational Auto-Encoders
William Harvey, Saeid Naderiparizi, Frank Wood
We present a conditional variational auto-encoder (VAE) which, to avoid the substantial cost of training from scratch, uses an architecture and training objective capable of levera…
Probabilistic Surrogate Networks for Simulators with Unbounded Randomness
Andreas Munk, Berend Zwartsenberg, Adam Ścibior +5
We present a framework for automatically structuring and training fast, approximate, deep neural surrogates of stochastic simulators. Unlike traditional approaches to surrogate mod…
Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation
Renhao Wang, Adam Scibior, Frank Wood
Imitation learning is a promising approach to end-to-end training of autonomous vehicle controllers. Typically the driving process with such approaches is entirely automatic and bl…
The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging
Andrew Warrington, Arthur Spencer, Frank Wood
We develop a stochastic whole-brain and body simulator of the nematode roundworm Caenorhabditis elegans (C. elegans) and show that it is sufficiently regularizing to allow imputati…
Amortized Monte Carlo Integration
Adam Goliński, Frank Wood, Tom Rainforth
Current approaches to amortizing Bayesian inference focus solely on approximating the posterior distribution. Typically, this approximation is, in turn, used to calculate expectati…
Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training
William Harvey, Michael Teng, Frank Wood
Hard visual attention is a promising approach to reduce the computational burden of modern computer vision methodologies. Hard attention mechanisms are typically non-differentiable…
Imitation Learning of Factored Multi-agent Reactive Models
Michael Teng, Tuan Anh Le, Adam Scibior +1
We apply recent advances in deep generative modeling to the task of imitation learning from biological agents. Specifically, we apply variations of the variational recurrent neural…
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…
Inference Trees: Adaptive Inference with Exploration
Tom Rainforth, Yuan Zhou, Xiaoyu Lu +4
We introduce inference trees (ITs), a new class of inference methods that build on ideas from Monte Carlo tree search to perform adaptive sampling in a manner that balances explora…