Purdue University

In Print: ‘Sparse Graphical Modeling for High Dimensional Data’

This book offers a comprehensive framework for mastering the complexities of learning high-dimensional sparse graphical models through the use of conditional independence tests. These tests are ...
Forbes

Sustainable AI Turns Small Data Into Huge Results For Manufacturers And Researchers

Sparse modeling AI is edging out traditional deep learning to become the technology of choice for product manufacturers and medical researchers because it ticks off all the boxes for modern quality ...
Semiconductor Engineering

HW-Aligned Sparse Attention Architecture For Efficient Long-Context Modeling (DeepSeek et al.)

A new technical paper titled “Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention” was published by DeepSeek, Peking University and University of Washington.
Nature

Sparse Modeling delivers fast, energy efficient and explainable AI solutions for cutting-edge medical applications

Japanese AI specialist HACARUS Inc. is successfully utilizing its proprietary Sparse Modeling AI technology in joint projects including phenotype drug discovery, medical imaging and ECG monitors for ...
Forbes

Getting Serious About Handling Sparse Models! Re-Examining Dense Tensors In The AI Age

What can you do about data sparsity? What do you do when you have a matrix with a bunch of zeros in it, and you can't get a good look at a complex system because so many of the nodes are empty? Matrix ...
Business Wire

Cerebras and Neural Magic Unlock the Power of Sparse LLMs for Faster, More Power Efficient, Lower Cost AI Model Training and Deployment

SUNNYVALE, Calif. & CAMBRIDGE, Mass.--(BUSINESS WIRE)--Cerebras Systems, the pioneer in accelerating generative AI, and Neural Magic, a leader in high-performance enterprise inference servers, today ...
EurekAlert!

Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables

Dynamical systems modeling is one of the most successfully implemented methodologies throughout mathematical oncology (1). Applications of these model first approaches have led to important insights ...