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Found 4 papers, 1 papers with code
Disentangling representations in Restricted Boltzmann Machines without adversaries
A goal of unsupervised machine learning is to build representations of complex high-dimensional data, with simple relations to their properties.
Inferring epistasis from genomic data with comparable mutation and outcrossing rate
We consider a population evolving due to mutation, selection and recombination, where selection includes single-locus terms (additive fitness) and two-loci terms (pairwise epistatic fitness).
'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.
Inverse Statistical Physics of Protein Sequences: A Key Issues Review
In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved.
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