Machine Learning Gets a Robustness Boost by Turning Labels into Preferences
Researchers have recast multi-label classification as an order structure learning problem, yielding classifiers that better withstand noisy and imbalanced labels ...
Researchers have recast multi-label classification as an order structure learning problem, yielding classifiers that better withstand noisy and imbalanced labels ...
Researchers at North Carolina State University have developed S-DEIM, a method that reconstructs global sea surface temperatures from sparse observations ...
Researchers have combined scanning X-ray scattering and fluorescence with machine learning to map the multi-scale structure of yellow pea seeds ...
Researchers have combined quantum-mechanical simulations, neural networks, and Bayesian statistics to cut the voltage prediction error of a physics-based lithium-ion ...
A rapid review of twelve studies finds that artificial intelligence can sharpen health predictions from weather data, but poor reporting ...
A new Nature Machine Intelligence study provides causal evidence that language models actively use internal confidence signals to shape their ...
Researchers in Xi'an have developed an information-theory-based anomaly detection method that outperforms state-of-the-art algorithms on critical industrial control system benchmarks.
A new Nature Neuroscience study shows that olfactory learning reshapes the low-dimensional neural manifolds storing odor memories, optimizing their geometry ...
A new machine learning study maps how snowglow and tourism-driven urbanization are brightening the night skies above Türkiye's two premier ...
A new review outlines how structural health monitoring sensors, tested from Alaskan pipelines to space rockets, could detect damage and ...
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© 2025 Scienmag - Science Magazine
© 2025 Scienmag - Science Magazine