18 citations · 32 across the 14 of their papers we have counts for
15 papers
A Hierarchical Importance-Guided Multi-objective Evolutionary Framework for Deep Neural Network Pruning
Zak Khan, Azam Asilian Bidgoli
The optimization of over-parameterized deep neural networks represents a large-scale, high-dimensional, and strongly non-convex decision problem that challenges existing optimizati…
Med-SegLens: Latent-Level Model Diffing for Interpretable Medical Image Segmentation
Salma J. Ahmed, Emad A. Mohammed, Azam Asilian Bidgoli
Modern segmentation models achieve strong predictive performance but remain largely opaque, limiting our ability to diagnose failures, understand dataset shift, or intervene in a p…
EvoPS: Evolutionary Patch Selection for Whole Slide Image Analysis in Computational Pathology
Saya Hashemian, Azam Asilian Bidgoli
In computational pathology, the gigapixel scale of Whole-Slide Images (WSIs) necessitates their division into thousands of smaller patches. Analyzing these high-dimensional patch e…
J-RAS: Mutual Adaptation for Medical Image Segmentation via Contrastive Retrieval-Augmented Joint Optimization
Salma J. Ahmed, Emad A. Mohammed, Azam Asilian Bidgoli
Manual medical image segmentation by clinicians, though accurate, is time-consuming and variable across experts, while AI-based models automate this process but often falter under…
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 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…
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…
Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images
Azam Asilian Bidgoli, Shahryar Rahnamayan, Taher Dehkharghanian +2
One of the main obstacles of adopting digital pathology is the challenge of efficient processing of hyperdimensional digitized biopsy samples, called whole slide images (WSIs). Exp…
Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology
Pooria Mazaheri, Azam Asilian Bidgoli, Shahryar Rahnamayan +1
Recently, deep learning has started to play an essential role in healthcare applications, including image search in digital pathology. Despite the recent progress in computer visio…
Image-Based Benchmarking and Visualization for Large-Scale Global Optimization
Kyle Robert Harrison, Azam Asilian Bidgoli, Shahryar Rahnamayan +1
In the context of optimization, visualization techniques can be useful for understanding the behaviour of optimization algorithms and can even provide a means to facilitate human i…
Towards Solving Large-scale Expensive Optimization Problems Efficiently Using Coordinate Descent Algorithm
Shahryar Rahnamayan, Seyed Jalaleddin Mousavirad
Many real-world problems are categorized as large-scale problems, and metaheuristic algorithms as an alternative method to solve large-scale problem; they need the evaluation of ma…