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stat.ML2025
Learning curves theory for hierarchically compositional data with power-law distributed features
Francesco Cagnetta, Hyunmo Kang, Matthieu Wyart
Recent theories suggest that Neural Scaling Laws arise whenever the task is linearly decomposed into power-law distributed units. Alternatively, scaling laws also emerge when data…
stat.ML2025
How Compositional Generalization and Creativity Improve as Diffusion Models are Trained
Alessandro Favero, Antonio Sclocchi, Francesco Cagnetta +2
Natural data is often organized as a hierarchical composition of features. How many samples do generative models need in order to learn the composition rules, so as to produce a co…