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Found 6 papers, 1 papers with code
Value Function Decomposition for Iterative Design of Reinforcement Learning Agents
These value estimates provide insight into an agent's learning and decision-making process and enable new training methods to mitigate common problems.
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
Calcium imaging permits optical measurement of neural activity.
Linear dynamical neural population models through nonlinear embeddings
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.
Black box variational inference for state space models
These models have the advantage of learning latent structure both from noisy observations and from the temporal ordering in the data, where it is assumed that meaningful correlation structure exists across time.
Bayesian Entropy Estimation for Countable Discrete Distributions
The Pitman-Yor process, a generalization of Dirichlet process, provides a tractable prior distribution over the space of countably infinite discrete distributions, and has found major applications in Bayesian non-parametric statistics and machine learning.
Information Theory Information Theory
Bayesian estimation of discrete entropy with mixtures of stick-breaking priors
We consider the problem of estimating Shannon's entropy H in the under-sampled regime, where the number of possible symbols may be unknown or countably infinite.
