With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …

Abstract Background and Objective: Artificial Intelligence (AI) has shown significant
advancements across several industries, including healthcare, using better fusion …

Machine-learning models have demonstrated great success in learning complex patterns
that enable them to make predictions about unobserved data. In addition to using models for …

Vision-language pre-trained models have achieved impressive performance on various
downstream tasks. However their large model sizes hinder their utilization on platforms with …

Machine-learning models have demonstrated great success in learning complex patterns
that enable them to make predictions about unobserved data. In addition to using models for …

We propose ResRep, a novel method for lossless channel pruning (aka filter pruning), which
slims down a CNN by reducing the width (number of output channels) of convolutional …

Convolutional neural networks (CNNs) have shown promising results and have
outperformed classical machine learning techniques in tasks such as image classification …

The redundancy is widely recognized in Convolutional Neural Networks (CNNs), which
enables to remove some unimportant filters from convolutional layers so as to slim the …

It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the
optimal number of filters (ie, the width) at each layer is tricky, given an architecture; and 2) …

The current trend of pushing CNNs deeper with convolutions has created a pressing
demand to achieve higher compression gains on CNNs where convolutions dominate the …