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06/23/2022
Disentangling representations in Restricted Boltzmann Machines without adversaries
A goal of unsupervised machine learning is to disentangle representation...
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11/20/2020
Low-Dimensional Manifolds Support Multiplexed Integrations in Recurrent Neural Networks
We study the learning dynamics and the representations emerging in Recur...
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12/30/2019
'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 invarian...
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02/18/2019
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...
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11/21/2016
Emergence of Compositional Representations in Restricted Boltzmann Machines
Extracting automatically the complex set of features composing real high...
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04/19/2011
High-Dimensional Inference with the generalized Hopfield Model: Principal Component Analysis and Corrections
We consider the problem of inferring the interactions between a set of N...
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02/25/2011
Fast Inference of Interactions in Assemblies of Stochastic Integrate-and-Fire Neurons from Spike Recordings
We present two Bayesian procedures to infer the interactions and externa...
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10/26/2010
