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cs.LG2025
Implicit bias produces neural scaling laws in learning curves, from perceptrons to deep networks
Francesco D'Amico, Dario Bocchi, Matteo Negri
Scaling laws in deep learning -- empirical power-law relationships linking model performance to resource growth -- have emerged as simple yet striking regularities across architect…
cs.LG2024
Self-attention as an attractor network: transient memories without backpropagation
Francesco D'Amico, Matteo Negri
Transformers are one of the most successful architectures of modern neural networks. At their core there is the so-called attention mechanism, which recently interested the physics…