ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...

In this study, we focus on investigating a nonsmooth convex optimization problem involving the l 1-norm under a non-negative constraint, with the goal of developing an inverse-problem solver for image ...

1 The School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, China 2 The School of Intelligent Manufacturing, Chongqing Industry and Trade Polytechnic, Chongqing, ...

Learn how gradient descent really works by building it step by step in Python. No libraries, no shortcuts—just pure math and code made simple. Trump's sons distance themselves from new Trump-branded ...

Abstract: This paper presents an innovative algorithm that combines mini-batch gradient descent with adaptive techniques to enhance the accuracy and efficiency of localization in complex environments.

Hi, I noticed that for Llama2(forget10), gradient difference shows much lower model utility (~0.27) than gradient ascent (~0.63) in the leaderboard. This seems unusual since gradient difference is ...

Abstract: Caching integrated with recommendation systems has emerged as a prominent trend in the caching research field, particularly concerning the volume of data within the network and diverse user ...