15 citations · 15 across the 4 of their papers we have counts for
5 papers
A Unified Theory of Exact Inference and Learning in Exponential Family Latent Variable Models
Sacha Sokoloski
Bayes' rule describes how to infer posterior beliefs about latent variables given observations, and inference is a critical step in learning algorithms for latent variable models (…
A computational approach to visual ecology with deep reinforcement learning
Sacha Sokoloski, Jure Majnik, Philipp Berens
Animal vision is thought to optimize various objectives from metabolic efficiency to discrimination performance, yet its ultimate objective is to facilitate the survival of the ani…
Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering
Sacha Sokoloski, Philipp Berens
We introduce hierarchical mixtures of Gaussians (HMoGs), which unify dimensionality reduction and clustering into a single probabilistic model. HMoGs provide closed-form expression…
Conditional Finite Mixtures of Poisson Distributions for Context-Dependent Neural Correlations
Sacha Sokoloski, Ruben Coen-Cagli
Parallel recordings of neural spike counts have revealed the existence of context-dependent noise correlations in neural populations. Theories of population coding have also shown…
A Biologically Realistic Model of Saccadic Eye Control with Probabilistic Population Codes
Sacha Sokoloski
The posterior parietal cortex is believed to direct eye movements, especially in regards to target tracking tasks, and a number of debates exist over the precise nature of the comp…