This repository collects research code developed during my PhD on Riemannian optimization and low-rank methods for machine learning. It includes Python modules for optimization on matrix manifolds, ...

AMD and Intel have now published a full technical specification for ACE — AI Compute Extensions — the most significant overhaul to x86 AI compute in the architecture's history, co-authored by eight ...

D-Matrix says its chips can run inference workloads 10 times faster and using five times less energy than a standalone graphics processing unit from Nvidia. Like Cerebras, D-Matrix is trying to prove ...

Abstract: Matrix factorization is a fundamental characterization model in machine learning and is usually solved using mathematical decomposition reconstruction loss. However, matrix factorization is ...

The extracellular matrix is a complex network of material such as proteins and polysaccharides that are secreted locally by cells and remain closely associated with them to provide structural, ...

Abstract: The semi-supervised multiview clustering (MVC) methods based on non-negative matrix factorization (NMF) have attracted considerable attention due to their ability to utilize partial ...

PhAST is a Python library built on PyTorch that allows you to solve phase-field fracture problems without assembling large global matrices. It is designed to be differentiable, making it easier to ...