4 citations · 13 across the 9 of their papers we have counts for
9 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…
Enhancing Diversity in Multi-objective Feature Selection
Sevil Zanjani Miyandoab, Shahryar Rahnamayan, Azam Asilian Bidgoli +2
Feature selection plays a pivotal role in the data preprocessing and model-building pipeline, significantly enhancing model performance, interpretability, and resource efficiency a…
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…
Training Artificial Neural Networks by Coordinate Search Algorithm
Ehsan Rokhsatyazdi, Shahryar Rahnamayan, Sevil Zanjani Miyandoab +2
Training Artificial Neural Networks poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic G…
Compact NSGA-II for Multi-objective Feature Selection
Sevil Zanjani Miyandoab, Shahryar Rahnamayan, Azam Asilian Bidgoli
Feature selection is an expensive challenging task in machine learning and data mining aimed at removing irrelevant and redundant features. This contributes to an improvement in cl…
Multi-objective Binary Coordinate Search for Feature Selection
Sevil Zanjani Miyandoab, Shahryar Rahnamayan, Azam Asilian Bidgoli
A supervised feature selection method selects an appropriate but concise set of features to differentiate classes, which is highly expensive for large-scale datasets. Therefore, fe…