With the projected global surge in hydrogen demand, driven by increasing applications and
the imperative for low-emission hydrogen, the integration of machine learning (ML) across …

As a classical machine learning model, support vector machine (SVM) has attracted much
attention due to its rigorous theoretical foundation and powerful discriminative performance …

Symmetric eigenvalue decomposition (EVD) is a fundamental analytic and numerical tool
used in many scientific areas. The state-of-the-art algorithm in terms of performance is …

Benefiting from the advancement of hardware accelerators such as GPUs, deep neural
networks and scientific computing applications can achieve superior performance. Recently …

Support vector machine (SVM) is one of the most classical machine learning algorithms,
which performs well in many fields. However, the traditional training algorithms are not …

The large p small n problem is a challenge without a de facto standard method available to
it. In this study, we propose a tensor-decomposition (TD)-based unsupervised feature …

Abstract Machine learning (ML) on Big Data has gone beyond the capacity of traditional
machines and technologies. ML for large scale datasets is the current focus of researchers …

The emergence of neural engines such as Nvidia TensorCore GPU brings a revolution to
deep neural networks, as the neural engines can perform extremely fast general matrix …