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cs.LG2024
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 (…
cs.LG2022
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…
cs.LG2019★ 15 cited
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…