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Found 4 papers, 2 papers with code
Revisiting local branching with a machine learning lens
In this work, we study the relation between the size of the search neighborhood and the behavior of the underlying LB algorithm, and we devise a leaning based framework for predicting the best size for the specific instance to be solved.
Embedded hyper-parameter tuning by Simulated Annealing
We propose a new metaheuristic training scheme that combines Stochastic Gradient Descent (SGD) and Discrete Optimization in an unconventional way.
Faster SGD training by minibatch persistency
It is well known that, for most datasets, the use of large-size minibatches for Stochastic Gradient Descent (SGD) typically leads to slow convergence and poor generalization.
Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study
A commonly-used nonlinear operator is the so-called rectified linear unit (ReLU), whose output is just the maximum between its input value and zero.
