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Found 4 papers, 1 papers with code
'Place-cell' emergence and learning of invariant data with restricted Boltzmann machines: breaking and dynamical restoration of continuous symmetries in the weight space
Distributions of data or sensory stimuli often enjoy underlying invariances.
Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins
A Restricted Boltzmann Machine (RBM) is an unsupervised machine-learning bipartite graphical model that jointly learns a probability distribution over data and extracts their relevant statistical features.
Learning protein constitutive motifs from sequence data
Statistical analysis of evolutionary-related protein sequences provides insights about their structure, function, and history.
Emergence of Compositional Representations in Restricted Boltzmann Machines
Extracting automatically the complex set of features composing real high-dimensional data is crucial for achieving high performance in machine--learning tasks.
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