147 citations · 719 across the 43 of their papers we have counts for
4 papers · 1 filter
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…
Streaming Inference for Infinite Non-Stationary Clustering
Rylan Schaeffer, Gabrielle Kaili-May Liu, Yilun Du +2
Learning from a continuous stream of non-stationary data in an unsupervised manner is arguably one of the most common and most challenging settings facing intelligent agents. Here,…
Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models
Yixin Wang, Anthony Degleris, Alex H. Williams +1
Neyman-Scott processes (NSPs) are point process models that generate clusters of points in time or space. They are natural models for a wide range of phenomena, ranging from neural…