81 citations · 132 across the 11 of their papers we have counts for
6 papers · 1 filter
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…