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Quantum machine learning nears practicality as partial error correction reduces hardware demands
Imagine a future where quantum computers supercharge machine learning—training models in seconds, extracting insights from ...
An AI-driven computational toolkit, Gcoupler, integrates ligand design, statistical modeling, and graph neural networks to predict endogenous metabolites that allosterically modulate the GPCR–Gα ...
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Overparameterized neural networks: Feature learning precedes overfitting, research finds
Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...
Autonomous driving systems increasingly rely on data-driven approaches, yet many still struggle with reasoning, handling rare scenarios, and transparently explaining their actions. A new study ...
Recent advances in artificial intelligence (AI) and machine learning (ML) have transformed our ability to decode complex ...
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