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Found 3 papers, 2 papers with code
Les Houches Lectures on Deep Learning at Large & Infinite Width
These lectures, presented at the 2022 Les Houches Summer School on Statistical Physics and Machine Learning, focus on the infinite-width limit and large-width regime of deep neural networks.
Asymptotic Characterisation of Robust Empirical Risk Minimisation Performance in the Presence of Outliers
We study robust linear regression in high-dimension, when both the dimension $d$ and the number of data points $n$ diverge with a fixed ratio $\alpha=n/d$, and study a data model that includes outliers.
Intrinsic dimension estimation for locally undersampled data
Traditionally, most IDE algorithms are either based on multiscale principal component analysis (PCA) or on the notion of correlation dimension (and more in general on k-nearest-neighbors distances).
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