Machine learning techniques that make use of tensor networks could manipulate data more efficiently and help open the black ...

Foundation models are AI systems trained on vast amounts of data — often trillions of individual data points — and they are capable of learning new ways of modeling information and performing a range ...

Abstract: The computation of matrix pseudoinverses is a recurrent requirement across various scientific computing and engineering domains. The prevailing models for matrix pseudoinverse typically ...

In this paper, we introduce R-NET, an end-to-end neural networks model for reading comprehension style question answering, which aims to answer questions from a given passage. We first match the ...

Abstract: Federated tuning of large models is an emerging paradigm that pushes the promising generative AI services into the network edge. However, as model sizes scale up, the conflicts between their ...