Xiangtian Hui Examines Machine Learning and Data Engineering Methods for Healthcare Data Systems and Cancer Informatics

A body of healthcare AI research connects data infrastructure, machine learning, and natural language processing across ...

Quantum advantage reassessed: More realistic benchmarks for quantum algorithms

Quantum advantage refers to the point at which a quantum computer solves a clearly defined task faster or more efficiently than any classical computer—or makes it solvable in the first place. For many ...
Frontiers

Machine Learning and Observational Data for Clinical Trial Optimization

Clinical research has long rested on a familiar triad: the randomized trial, the carefully curated cohort, and the cross-sectional survey. Each has its ...
News-Medical.Net

AI-driven optimization speeds large-scale vaccine distribution planning

Optimization models do an excellent job of ensuring products get where they are needed in the most efficient way possible, ...
TMCnet

WiMi Explores Federated Training Framework for Hybrid Quantum-Classical Machine Learning Models

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, is exploring a federated training framework for hybrid ...

A public health challenge has led to a more efficient way to allocate all sorts of resources

Optimization models do an excellent job of ensuring products get where they are needed in the most efficient way possible, ...
The New York Times

U.S. soccer’s ‘pay-to-play’ problem: The lightning-rod issue explained in a World Cup context

Youth soccer clubs Nomads and South Florida Football Academy in the 2021 MLS Next Cup Tim Heitman / USA TODAY Sports The inquest began before the final whistle even sounded, before Belgium ...
PNAS

Demonstrating real advantage of machine learning–enhanced Monte Carlo for combinatorial optimization

In this work, we address a question that has attracted intense interest in recent years: whether machine learning-assisted algorithms can genuinely outperform classical approaches in challenging ...
Eos

Collinearity is Not Always a Problem in Machine Learning

The workflow used in the paper is illustrated here, showing how synthetic geological models were constructed and how their geophysical properties were derived via forward simulation. Then, different ...
Phys.org

Machine learning helps solve a central problem of quantum chemistry

Within the STRUCTURES Cluster of Excellence, two research teams at the Interdisciplinary Center for Scientific Computing (IWR) have refined a computing process, long held to be unreliable, such that ...
VentureBeat

Google’s ‘Nested Learning’ paradigm could solve AI's memory and continual learning problem

Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after ...