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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.
Origami in N dimensions: How feed-forward networks manufacture linear separability
TL;DR: Shows that the internal processing of deep networks can be thought of as literal folding operations on the data distribution in the N-dimensional activation space.
Decomposing neural networks as mappings of correlation functions
Understanding the functional principles of information processing in deep neural networks continues to be a challenge, in particular for networks with trained and thus non-random weights.
Unfolding recurrence by Green’s functions for optimized reservoir computing
Cortical networks are strongly recurrent, and neurons have intrinsic temporal dynamics.
Unfolding recurrence by Green's functions for optimized reservoir computing
Cortical networks are strongly recurrent, and neurons have intrinsic temporal dynamics.
