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stat.ML2026
Memorisation, convergence and generalisation in generative models
Antoine Maillard, Sebastian Goldt
Generative neural networks learn how to produce highly realistic images from a large, but finite number of examples - or do they simply memorise their training set? To settle this…
stat.ML2026
Factual recall in linear associative memories: sharp asymptotics and mechanistic insights
Alessio Giorlandino, Sebastian Goldt, Antoine Maillard
Large language models demonstrate remarkable ability in factual recall, yet the fundamental limits of storing and retrieving input--output associations with neural networks remain…
stat.ML2026
A Noise Sensitivity Exponent Controls Large Statistical-to-Computational Gaps in Single- and Multi-Index Models
Leonardo Defilippis, Florent Krzakala, Bruno Loureiro +1
Understanding when learning is statistically possible yet computationally hard is a central challenge in high-dimensional statistics. In this work, we investigate this question in…