Computation has profoundly shaped the way we approach sensing. In the realm of biosensing, for example, signals are acquired—often at high cost—with various sources of noise, including the stochastic ...

Federated learning (FL) has emerged as a popular machine learning paradigm which allows multiple data owners to train models collaboratively with out sharing their raw datasets. It holds potential for ...

In clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power.

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...

A new technical paper titled “Scaling Deep Learning Computation over the Inter-Core Connected Intelligence Processor” was published by researchers at UIUC and Microsoft Research. “As AI chips ...

Artificial Deep Neural Networks (DNNs) and, more recently, large-scale foundation models have made breakthrough progress drawing loose structural and ...