Technology Networks

Deep Learning for Variant Calling: How DeepVariant and Neural Networks Are Improving Genomic Accuracy

Deep learning variant calling has transformed genomic accuracy. Discover how DeepVariant works, outperforms classical tools, ...
Communications of the ACM

Can We Understand How Large Language Models Reason?

Large language models can write essays, solve math problems, and generate computer code, but it’s not fully understood how ...
Tech Xplore

Researchers discover a smarter way to solve vehicle routing problems using adaptive swarm learning

Combinatorial optimization problems are often encountered in real-world applications, including logistics, scheduling and ...

AI stock trading startup EquiLibre raises funding at a $500M+ valuation

Prague-based artificial intelligence developer EquiLibre Technologies Inc. has raised funding at a valuation exceeding $500 ...

Cognitive flexibility problems may arise months before memory impairment in Alzheimer's

When most people think about Alzheimer's disease, memory loss is usually the first thing that comes to mind. Forgetting a loved one's name, missing appointments or repeatedly misplacing everyday items ...

Cochlea network model reveals how inner ear may sort sound from noise

Over 70 million people in the U.S. are impacted by hearing loss, and age-related hearing loss is the second most common ...
TMCnet

WiMi Explores the Application of Neural Networks in Parameter Optimization for Dual-Field Quantum Key Distribution

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, announced that they are researching the use of neural ...
Fabbaloo

Deep Learning Spots 3D Print Defects In Process

A new study explores deep learning for image-based defect detection during 3D printing, looking to catch bad builds.
Frontiers

Implementing physics-informed neural networks with deep learning for differential equations

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...