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A Unified Theory of Exact Inference and Learning in Exponential Family Latent Variable Models
In this paper we study the line that separates LVMs that rely on approximation schemes from those that do not, and develop a general theory of exponential family, latent variable models for which inference and learning may be implemented exactly.
A computational approach to visual ecology with deep reinforcement learning
To study how environments shape and constrain visual processing, we developed a deep reinforcement learning framework in which an agent moves through a 3-d environment that it perceives through a vision model, where its only goal is to survive.
Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering
Here, we show how a family of such two-stage models can be combined into a single, hierarchical model that we call a hierarchical mixture of Gaussians (HMoG).
Conditional Finite Mixtures of Poisson Distributions for Context-Dependent Neural Correlations
Parallel recordings of neural spike counts have revealed the existence of context-dependent noise correlations in neural populations.
Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics
In order to interact intelligently with objects in the world, animals must first transform neural population responses into estimates of the dynamic, unknown stimuli which caused them.
