Moving heavy materials through cutting, polishing and coating stages requires precise balancing of load capacity and motion speed. Here’s how the right linear guidance selection and configuration can ...

This is the official implementation of our paper "Riemannian Optimization on Relaxed Indicator Matrix Manifold" . We propose a fundamental manifold in machine learning—the Relaxed Indicator Matrix ...

Understanding the mechanism of how neural networks learn features from data is a fundamental problem in machine learning. Our work explicitly connects the mechanism of neural feature learning to a ...

Abstract: This study addresses critical gaps in financial risk assessment and portfolio optimization by integrating advanced machine learning (ML) and deep learning (DL) techniques to handle the ...

The minimization of matrix bandwidth is a cornerstone challenge in computational linear algebra and graph theory, with direct implications for the efficiency of numerical solvers, finite-element ...

Deep neural networks (DNNs) have achieved remarkable success across various fields, including computer vision, natural language processing, and speech recognition. This success is largely attributed ...

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Abstract: Matrix-variable optimization is a generalization of vector-variable optimization and has been found to have many important applications. To reduce computation time and storage requirement, ...

ABSTRACT: The positive-definiteness and sparsity are the most important property of high-dimensional precision matrices. To better achieve those property, this paper uses a sparse lasso penalized ...