Code and Data Repository for Multi-Objective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural Networks

The goal of this script is to train neural networks with integer weights with MILP models and to test their accuracy.

Authors

Neil Yorke‐Smith (ORCID: https://orcid.org/0000-0002-1814-3515)
Hoong Chuin Lau (ORCID: https://orcid.org/0000-0002-5326-411X)
Simone Milanesi (ORCID: https://orcid.org/0000-0002-6314-1965)
Ambrogio Maria Bernardelli (ORCID: https://orcid.org/0000-0002-2328-7062)
Stefano Gualandi (ORCID: https://orcid.org/0000-0002-2111-3528)

Publication Details

Journal: INFORMS journal on computing
Published: 2024-09-13
DOI: https://doi.org/10.1287/ijoc.2023.0281.cd
Primary Topic: Neural Networks and Applications
Type: article
Field-Weighted Citation Impact: 0.00

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article

Code and Data Repository for Multi-Objective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural Networks

Neil Yorke‐Smith, Hoong Chuin Lau, Simone Milanesi, Ambrogio Maria Bernardelli et al.

Sep 13, 2024

INFORMS journal on computing

Neural Networks and Applications

article

Code and Data Repository for Multi-Objective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural Networks

Neil Yorke‐Smith, Hoong Chuin Lau, Simone Milanesi, Ambrogio Maria Bernardelli, Stefano Gualandi

September 13, 2024article en

Abstract

The goal of this script is to train neural networks with integer weights with MILP models and to test their accuracy.

INFORMS journal on computing

Openalex Percentile: Top 86%

Neural Networks and Applications

DOI Source OpenAlex

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Code and Data Repository for Multi-Objective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural Networks — Neil Yorke‐Smith, Hoong Chuin Lau, et al. · INFORMS journal on computing (2024) | TGRS Research Map | TGRS