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How do neural networks learn? A mathematical formula explains how they detect relevant patterns

Top eigenvector of AGOP of two separate models, MLPs and Laplace kernel machines, captured similar features (cosine similarity greater than .99) when trained on the same data from CelebA across various tasks. Credit: Science (2024). DOI: 10.1126/science.adi5639 Neural networks have been powering breakthroughs in artificial intelligence, including the large language models that are now being used in a wide range of applications, from finance, to human resources to health care. But these networks ...

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