Mirror Trick Lets AI Learn Messy Networks Without Any Labels
Researchers have developed BHyGNN+, a self-supervised framework that learns representations of heterophilic hypergraphs without labels by contrasting each hypergraph against ...
Researchers have developed BHyGNN+, a self-supervised framework that learns representations of heterophilic hypergraphs without labels by contrasting each hypergraph against ...
Researchers in Shanghai have developed TECL, a machine learning framework that treats conflicting opinions across multiple data views as valuable ...
A new review synthesises how artificial intelligence, deep learning, and Internet of Things architectures are transforming automated crop pest detection ...
A random forest analysis of 260 Filipino first-year college students found that eight demographic variables explained almost none of the ...
Researchers in China have unveiled SpecMamba-Net, a dual-branch Mamba-based AI that fuses frequency-domain texture cues with linear-complexity global state-space modeling ...
Researchers in China have developed a neighbor-constrained attention framework that improves how artificial intelligence classifies gigapixel whole-slide pathology images by ...
Researchers at UC Berkeley used active learning and a model called ADhunter to discover and characterize nearly 10,000 transcriptional activator ...
A feasibility study on Italy's Po Delta combined bridge cameras, drones, satellites, ship sampling and hydrodynamic modelling to track plastic ...
A new framework called FuseDepth fuses frozen detection, matting and surface-normal models with an adapted Depth Anything backbone to achieve ...
A new study finds that random forest models outperform neural networks in predicting arsenic contamination across Ghana's Pra, Ankobra, and ...
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© 2025 Scienmag - Science Magazine