Nature

Data-driven prediction of chemically relevant compositions in multi-component systems using tensor embeddings

The discovery of novel materials is crucial for developing new functional materials. This study introduces a predictive model designed to forecast complex multi-component oxide compositions, ...
Nature

Tensor decomposition of TMS-induced EEG oscillations reveals data-driven profiles of antiepileptic drug effects

Transcranial magnetic stimulation combined with electroencephalography is a powerful tool to probe human cortical excitability. The EEG response to TMS stimulation is altered by drugs active in the ...
EurekAlert!

Compression using automatically differentiable tensor networks: Fewer parameters with equal or better performance

(A) Illustration of a convolutional neural network (NN) whose variational parameters (T) are encoded in the automatically differentiable tensor network (ADTN) shown in (B). The ADTN contains many ...
Science Daily

New techniques efficiently accelerate sparse tensors for massive AI models

New computational techniques, 'HighLight' and 'Tailors and Swiftiles,' could dramatically boost the speed and performance of high-performance computing applications like graph analytics or generative ...

Anritsu Introduces Tensor VNA at IMS 2026, Redefining High Performance RF and Microwave Network Analysis

At IMS 2026, attendees will be able to see live demonstrations of the Tensor VNA, speak directly with Anritsu application experts, and learn how the platform helps reduce measurement uncertainty, ...