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Found 6 papers, 2 papers with code
The Copycat Perceptron: Smashing Barriers Through Collective Learning
We characterize the equilibrium properties of a model of $y$ coupled binary perceptrons in the teacher-student scenario, subject to a learning rule, with an explicit ferromagnetic coupling proportional to the Hamming distance between the students' weights.
Explaining the effects of non-convergent sampling in the training of Energy-Based Models
In this paper, we quantify the impact of using non-convergent Markov chains to train Energy-Based models (EBMs).
Thermodynamics of bidirectional associative memories
Introduced by Kosko in 1988 as a generalization of the Hopfield model to a bipartite structure, the simplest architecture is defined by two layers of neurons, with synaptic connections only between units of different layers: even without internal connections within each layer, information storage and retrieval are still possible through the reverberation of neural activities passing from one layer to another.
Inference in conditioned dynamics through causality restoration
Computing observables from conditioned dynamics is typically computationally hard, because, although obtaining independent samples efficiently from the unconditioned dynamics is usually feasible, generally most of the samples must be discarded (in a form of importance sampling) because they do not satisfy the imposed conditions.
Epidemic mitigation by statistical inference from contact tracing data
We conclude that probabilistic risk estimation is capable to enhance performance of digital contact tracing and should be considered in the currently developed mobile applications.
Loop corrections in spin models through density consistency
Computing marginal distributions of discrete or semidiscrete Markov random fields (MRFs) is a fundamental, generally intractable problem with a vast number of applications in virtually all fields of science.
