no code implementations • 4 Sep 2023 • Yasaman Bahri, Boris Hanin, Antonin Brossollet, Vittorio Erba, Christian Keup, Rosalba Pacelli, James B. Simon
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.
no code implementations • 21 Mar 2022 • Christian Keup, Moritz Helias
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.
no code implementations • 10 Feb 2022 • Kirsten Fischer, Alexandre René, Christian Keup, Moritz Layer, David Dahmen, Moritz Helias
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.
no code implementations • NeurIPS 2020 • Sandra Nestler, Christian Keup, David Dahmen, Matthieu Gilson, Holger Rauhut, Moritz Helias
Cortical networks are strongly recurrent, and neurons have intrinsic temporal dynamics.
no code implementations • 13 Oct 2020 • Sandra Nestler, Christian Keup, David Dahmen, Matthieu Gilson, Holger Rauhut, Moritz Helias
Cortical networks are strongly recurrent, and neurons have intrinsic temporal dynamics.