81 citations · 132 across the 11 of their papers we have counts for
11 papers
Towards Scalable and Stable Parallelization of Nonlinear RNNs
Xavier Gonzalez, Andrew Warrington, Jimmy T. H. Smith +1
Transformers and linear state space models can be evaluated in parallel on modern hardware, but evaluating nonlinear RNNs appears to be an inherently sequential problem. Recently,…
Switching Autoregressive Low-rank Tensor Models
Hyun Dong Lee, Andrew Warrington, Joshua I. Glaser +1
An important problem in time-series analysis is modeling systems with time-varying dynamics. Probabilistic models with joint continuous and discrete latent states offer interpretab…
Simplified State Space Layers for Sequence Modeling
Jimmy T. H. Smith, Andrew Warrington, Scott W. Linderman
Models using structured state space sequence (S4) layers have achieved state-of-the-art performance on long-range sequence modeling tasks. An S4 layer combines linear state space m…
SIXO: Smoothing Inference with Twisted Objectives
Dieterich Lawson, Allan Raventós, Andrew Warrington +1
Sequential Monte Carlo (SMC) is an inference algorithm for state space models that approximates the posterior by sampling from a sequence of target distributions. The target distri…
Robust Asymmetric Learning in POMDPs
Andrew Warrington, J. Wilder Lavington, Adam Ścibior +2
Policies for partially observed Markov decision processes can be efficiently learned by imitating policies for the corresponding fully observed Markov decision processes. Unfortuna…
Coping With Simulators That Don't Always Return
Andrew Warrington, Saeid Naderiparizi, Frank Wood
Deterministic models are approximations of reality that are easy to interpret and often easier to build than stochastic alternatives. Unfortunately, as nature is capricious, observ…
Planning as Inference in Epidemiological Models
Frank Wood, Andrew Warrington, Saeid Naderiparizi +8
In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models. The kind of i…
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…
Generalising Cost-Optimal Particle Filtering
Andrew Warrington, Neil Dhir
We present an instance of the optimal sensor scheduling problem with the additional relaxation that our observer makes active choices whether or not to observe and how to observe.…
Updating the VESICLE-CNN Synapse Detector
Andrew Warrington, Frank Wood
We present an updated version of the VESICLE-CNN algorithm presented by Roncal et al. (2014). The original implementation makes use of a patch-based approach. This methodology is k…
On Nesting Monte Carlo Estimators
Tom Rainforth, Robert Cornish, Hongseok Yang +2
Many problems in machine learning and statistics involve nested expectations and thus do not permit conventional Monte Carlo (MC) estimation. For such problems, one must nest estim…