Search Results for author:
Found 28 papers, 2 papers with code
A Novel Structure-Agnostic Multi-Objective Approach for Weight-Sharing Compression in Deep Neural Networks
The experimental results show that we can reduce the neural network memory by $13. 72 \sim14. 98 \times$ on CIFAR-10, $11. 61 \sim 12. 99\times$ on CIFAR-100, and $7. 44 \sim 8. 58\times$ on ImageNet showcasing the effectiveness of the proposed deep neural network compression framework.
A Novel Pareto-optimal Ranking Method for Comparing Multi-objective Optimization Algorithms
This paper proposes a novel multi-metric comparison method to rank the performance of multi-/ many-objective optimization algorithms based on a set of performance indicators.
Large-scale Multi-objective Feature Selection: A Multi-phase Search Space Shrinking Approach
This paper proposes a novel large-scale multi-objective evolutionary algorithm based on the search space shrinking, termed LMSSS, to tackle the challenges of feature selection particularly as a sparse optimization problem.
Massive Dimensions Reduction and Hybridization with Meta-heuristics in Deep Learning
In this modern deep learning era, the state-of-the-art DNN models have millions and billions of parameters, including weights and biases, making them huge-scale optimization problems in terms of search space.
Enhancing Diversity in Multi-objective Feature Selection
The results demonstrate that replacing the last front of the population with an equivalent number of new random individuals generated using the genuine initialization method and featuring a limited number of features substantially improves the population's quality and, consequently, enhances the performance of the multi-objective algorithm.
Training Artificial Neural Networks by Coordinate Search Algorithm
Finding the optimal values for weights of ANNs is a large-scale optimization problem.
Multi-objective Binary Coordinate Search for Feature Selection
For this purpose, we have proposed the binary multi-objective coordinate search (MOCS) algorithm to solve large-scale feature selection problems.
Compact NSGA-II for Multi-objective Feature Selection
In this regard, we define feature selection as a multi-objective binary optimization task with the objectives of maximizing classification accuracy and minimizing the number of selected features.
ALFA -- Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals
This enables us to derive invariant features from training images without relying on training labels, thereby covering different abstraction levels.
Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology
By forcing the model to learn the ranking of matched outputs, the representation learning is customized toward image search instead of learning a class label.
Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images
Coarse multi-objective feature selection uses the reduced search space strategy guided by the classification accuracy and the number of features.
Semi-Supervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework
The generated pseudo labels of our proposed framework can be fed to various DNNs to improve their generalization capacity.
Ranked #1 on
Semi-Supervised Image Classification
on Salinas
(using extra training data)
Variable Functioning and Its Application to Large Scale Steel Frame Design Optimization
By using problem structure analysis technique and engineering expert knowledge, the $Fx$ method is used to enhance the steel frame design optimization process as a complex real-world problem.
Hospital-Agnostic Image Representation Learning in Digital Pathology
These variabilities are assumed to cause a domain shift in the images of different hospitals.
3D-RadViz: Three Dimensional Radial Visualization for Large-Scale Data Visualization
This paper presents 3D-RadViz, a visualization method for high dimensional data using a three dimensional radial visualization technique.
Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images
The feature extractor model is fine-tuned using hierarchical target labels of WSIs, i. e., anatomic site and primary diagnosis.
Forming Local Intersections of Projections for Classifying and Searching Histopathology Images
In this paper, we propose a novel image descriptor called Forming Local Intersections of Projections (FLIP) and its multi-resolution version (mFLIP) for representing histopathology images.
Image-Based Benchmarking and Visualization for Large-Scale Global Optimization
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 interaction with an optimizer.
Multi-objective Optimal Control of Dynamic Integrated Model of Climate and Economy: Evolution in Action
One of the widely used models for studying economics of climate change is the Dynamic Integrated model of Climate and Economy (DICE), which has been developed by Professor William Nordhaus, one of the laureates of the 2018 Nobel Memorial Prize in Economic Sciences.
Towards Solving Large-scale Expensive Optimization Problems Efficiently Using Coordinate Descent Algorithm
To the best our knowledge, there is no significant study to assess benchmark functions with various dimensions and landscape properties to investigate CD algorithm.
Learning Autoencoded Radon Projections
Autoencoders have been recently used for encoding medical images.
Opposition based Ensemble Micro Differential Evolution
Differential evolution (DE) algorithm with a small population size is called Micro-DE (MDE).
Classification and Retrieval of Digital Pathology Scans: A New Dataset
In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology.
Learning Opposites Using Neural Networks
In this paper, we introduce an approach to learn type-II opposites from the given inputs and their outputs using the artificial neural networks (ANNs).
Automated Resolution Selection for Image Segmentation
This paper introduces a framework for the automated selection of the best resolution for image segmentation.
Evolutionary Projection Selection for Radon Barcodes
A small number of equidistant projections, e. g., 4 or 8, is generally used to generate short barcodes.
Diversity Enhancement for Micro-Differential Evolution
Furthermore, comprehensive comparative simulations and analysis on performance of the MDE algorithms over various mutation schemes, population sizes, problem types (i. e. uni-modal, multi-modal, and composite), problem dimensionalities, and mutation factor ranges are conducted by considering population diversity analysis for stagnation and trapping in local optimum situations.
Learning Opposites with Evolving Rules
This, of course, is a very naive estimate of the actual or true (non-linear) opposite $\breve{x}_{II}$, which has been called type-II opposite in literature.
