4 papers
Evolutionary Feature-wise Thresholding for Binary Representation of NLP Embeddings
Soumen Sinha, Shahryar Rahnamayan, Azam Asilian Bidgoli
Efficient text embedding is crucial for large-scale natural language processing (NLP) applications, where storage and computational efficiency are key concerns. In this paper, we e…
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…
A Novel Pareto-optimal Ranking Method for Comparing Multi-objective Optimization Algorithms
Amin Ibrahim, Azam Asilian Bidgoli, Shahryar Rahnamayan +1
As the interest in multi- and many-objective optimization algorithms grows, the performance comparison of these algorithms becomes increasingly important. A large number of perform…
Large-scale Multi-objective Feature Selection: A Multi-phase Search Space Shrinking Approach
Azam Asilian Bidgoli, Shahryar Rahnamayan
Feature selection is a crucial step in machine learning, especially for high-dimensional datasets, where irrelevant and redundant features can degrade model performance and increas…