3 papers
cs.LG2026
Multi-Objective Reference-Aligned Machine Unlearning
Rasa Khosrowshahli, Stephen Asobiela, Beatrice Ombuki-Berman +1
Machine unlearning aims to remove the influence of specific training samples while preserving the model's utility. Existing single-objective approaches, such as gradient ascent or…
cs.CV2025
A Novel Structure-Agnostic Multi-Objective Approach for Weight-Sharing Compression in Deep Neural Networks
Rasa Khosrowshahli, Shahryar Rahnamayan, Beatrice Ombuki-Berman
Deep neural networks suffer from storing millions and billions of weights in memory post-training, making challenging memory-intensive models to deploy on embedded devices. The wei…
cs.NE2024
Massive Dimensions Reduction and Hybridization with Meta-heuristics in Deep Learning
Rasa Khosrowshahli, Shahryar Rahnamayan, Beatrice Ombuki-Berman
Deep learning is mainly based on utilizing gradient-based optimization for training Deep Neural Network (DNN) models. Although robust and widely used, gradient-based optimization a…