Multiobjective linear ensembles for robust and sparse training of few-bit neural networks

• Main Authors: BERNARDELLI, Ambrogio Maria, GUALANDI, Stefano, MILANESI, Simone, LAU, Hoong Chuin, NEIL, Yorke-Smith
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Binarized neural networks; Integer neural networks; Mixed-integer linear programming; Structured ensemble; Few-shot learning; Sparsity; Multi-objective optimisation; Artificial Intelligence and Robotics; Computer Sciences
• Online Access: https://ink.library.smu.edu.sg/sis_research/9955; https://ink.library.smu.edu.sg/context/sis_research/article/10955/viewcontent/2212.03659v2.pdf
• Institution: Singapore Management University

Training neural networks (NNs) using combinatorial optimization solvers has gained attention in recent years. In low-data settings, the use of state-of-the-art mixed integer linear programming solvers, for instance, has the potential to exactly train an NN while avoiding computing-intensive training...