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Found 4 papers, 3 papers with code
Unsupervised representation learning with recognition-parametrised probabilistic models
We introduce a new approach to probabilistic unsupervised learning based on the recognition-parametrised model (RPM): a normalised semi-parametric hypothesis class for joint distributions over observed and latent variables.
Structured Recognition for Generative Models with Explaining Away
A key goal of unsupervised learning is to go beyond density estimation and sample generation to reveal the structure inherent within observed data.
Probabilistic Tensor Decomposition of Neural Population Spiking Activity
The firing of neural populations is coordinated across cells, in time, and across experimentalconditions or repeated experimental trials; and so a full understanding of the computationalsignificance of neural responses must be based on a separation of these different contributions tostructured activity. Tensor decomposition is an approach to untangling the influence of multiple factors in data that iscommon in many fields.
Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference
Electroencephalographic (EEG) monitoring of neural activity is widely used for sleep disorder diagnostics and research.
