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Found 9 papers, 6 papers with code
Simplified State Space Layers for Sequence Modeling
Models using structured state space sequence (S4) layers have achieved state-of-the-art performance on long-range sequence modeling tasks.
Ranked #3 on
Long-range modeling
on LRA
SIXO: Smoothing Inference with Twisted Objectives
Sequential Monte Carlo (SMC) is an inference algorithm for state space models that approximates the posterior by sampling from a sequence of target distributions.
Robust Asymmetric Learning in POMDPs
Policies for partially observed Markov decision processes can be efficiently learned by imitating policies for the corresponding fully observed Markov decision processes.
Planning as Inference in Epidemiological Models
In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models.
Coping With Simulators That Don't Always Return
Deterministic models are approximations of reality that are easy to interpret and often easier to build than stochastic alternatives.
Coping With Simulators That Don’t Always Return
Deterministic models are approximations of reality that are often easier to build and interpret than stochastic alternatives.
The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging
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 imputation of latent membrane potentials from partial calcium fluorescence imaging observations.
Updating the VESICLE-CNN Synapse Detector
The original implementation makes use of a patch-based approach.
On Nesting Monte Carlo Estimators
Many problems in machine learning and statistics involve nested expectations and thus do not permit conventional Monte Carlo (MC) estimation.
