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09/11/2023
Stochastic Gradient Descent-like relaxation is equivalent to Glauber dynamics in discrete optimization and inference problems
Is Stochastic Gradient Descent (SGD) substantially different from Glaube...
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05/10/2023
Phase transitions in the mini-batch size for sparse and dense neural networks
The use of mini-batches of data in training artificial neural networks i...
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09/11/2022
Hard Optimization Problems have Soft Edges
Finding a Maximum Clique is a classic property test from graph theory; f...
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11/26/2021
Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization
Optimizing highly complex cost/energy functions over discrete variables ...
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12/10/2020
Learning from Survey Propagation: a Neural Network for MAX-E-3-SAT
Many natural optimization problems are NP-hard, which implies that they ...
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12/09/2020
Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes
Non-linear partial differential Kolmogorov equations are successfully us...
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03/27/2020
Large independent sets on random d-regular graphs with d small
In this paper, we present a prioritized local algorithm that computes a ...
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07/24/2018
Revisiting the Challenges of MaxClique
The MaxClique problem, finding the maximum complete subgraph in an E-R G...
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08/20/2015
