20 citations · 32 across the 5 of their papers we have counts for
14 papers
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…
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'…
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…