110 citations · 183 across the 5 of their papers we have counts for
5 papers
Value Function Decomposition for Iterative Design of Reinforcement Learning Agents
James MacGlashan, Evan Archer, Alisa Devlic +4
Designing reinforcement learning (RL) agents is typically a difficult process that requires numerous design iterations. Learning can fail for a multitude of reasons, and standard R…
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
Artur Speiser, Jinyao Yan, Evan Archer +3
Calcium imaging permits optical measurement of neural activity. Since intracellular calcium concentration is an indirect measurement of neural activity, computational tools are nec…
Linear dynamical neural population models through nonlinear embeddings
Yuanjun Gao, Evan Archer, Liam Paninski +1
A body of recent work in modeling neural activity focuses on recovering low-dimensional latent features that capture the statistical structure of large-scale neural populations. Mo…
Black box variational inference for state space models
Evan Archer, Il Memming Park, Lars Buesing +2
Latent variable time-series models are among the most heavily used tools from machine learning and applied statistics. These models have the advantage of learning latent structure…
Bayesian Entropy Estimation for Countable Discrete Distributions
Evan Archer, Il Memming Park, Jonathan Pillow
We consider the problem of estimating Shannon's entropy from discrete data, in cases where the number of possible symbols is unknown or even countably infinite. The Pitman-Yor…