2 papers
cs.LG2025
The Physics of Data and Tasks: Theories of Locality and Compositionality in Deep Learning
Alessandro Favero
Deep neural networks have achieved remarkable success, yet our understanding of how they learn remains limited. These models can learn high-dimensional tasks, which is generally st…
cs.LG2025
Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures
Francesco Cagnetta, Alessandro Favero, Antonio Sclocchi +1
How do neural language models acquire a language's structure when trained for next-token prediction? We address this question by deriving theoretical scaling laws for neural networ…