Learn how gradient descent really works by building it step by step in Python. No libraries, no shortcuts—just pure math and ...

This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...

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 ...

After Valve announced its upcoming Steam Machine living room box earlier this month, some analysts suggested to Ars that Valve could and should aggressively subsidize that hardware with “loss leader” ...

Introduction: Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as ...

Dwayne Johnson has been known worldwide as "The Rock," one of the greatest wrestlers the sport has ever seen. He also began to switch to a movie star in the early 2000s, and has since taken the world ...

Stochastic gradient descent (SGD) provides a scalable way to compute parameter estimates in applications involving large-scale data or streaming data. As an alternative version, averaged implicit SGD ...

ABSTRACT: The advent of the internet, as we all know, has brought about a significant change in human interaction and business operations around the world; yet, this evolution has also been marked by ...

SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data. The complex structure of cancer genomes means ...

Abstract: It is desirable in many multi-objective machine learning applications, such as multi-task learning with conflicting objectives, multi-objective reinforcement learning, to find a Pareto ...