158 citations · 1.4k across the 32 of their papers we have counts for
5 papers · 1 filter
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
Pratik Chaudhari, Anna Choromanska, Stefano Soatto +6
This paper proposes a new optimization algorithm called Entropy-SGD for training deep neural networks that is motivated by the local geometry of the energy landscape. Local extrema…
A method to reduce the rejection rate in Monte Carlo Markov Chains
Carlo Baldassi
We present a method for Monte Carlo sampling on systems with discrete variables (focusing in the Ising case), introducing a prior on the candidate moves in a Metropolis-Hastings sc…
Simultaneous identification of specifically interacting paralogs and inter-protein contacts by Direct-Coupling Analysis
Thomas Gueudré, Carlo Baldassi, Marco Zamparo +2
Understanding protein-protein interactions is central to our understanding of almost all complex biological processes. Computational tools exploiting rapidly growing genomic databa…
Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes
Carlo Baldassi, Christian Borgs, Jennifer Chayes +4
In artificial neural networks, learning from data is a computationally demanding task in which a large number of connection weights are iteratively tuned through stochastic-gradien…
Learning may need only a few bits of synaptic precision
Carlo Baldassi, Federica Gerace, Carlo Lucibello +2
Learning in neural networks poses peculiar challenges when using discretized rather then continuous synaptic states. The choice of discrete synapses is motivated by biological reas…