27 citations · 41 across the 9 of their papers we have counts for
9 papers
Discrete signaling mediates chaotic regularization in recurrent neural networks
Jan Bauer, Christian Keup, Jonathan Kadmon +1
Cortical circuits operate in a regime of intrinsic chaos, where even tiny changes in input can lead to divergent neural responses. Yet, remarkably, population codes in the brain va…
Kernel Renormalization in Bayesian Deep Neural Networks: the Equivalent Wishart Ansatz in the Proportional Regime
Paolo Baglioni, Christian Keup, Vincenzo Zimbardo +4
The scaling limit where both the size of the training set and the width of a deep neural network grow at the same rate, the so-called proportional-width regime, has been in…
Optimal thresholds and algorithms for a model of multi-modal learning in high dimensions
Christian Keup, Lenka Zdeborová
This work explores multi-modal inference in a high-dimensional simplified model, analytically quantifying the performance gain of multi-modal inference over that of analyzing modal…
Les Houches Lectures on Deep Learning at Large & Infinite Width
Yasaman Bahri, Boris Hanin, Antonin Brossollet +4
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…
The Distribution of Unstable Fixed Points in Chaotic Neural Networks
Jakob Stubenrauch, Christian Keup, Anno C. Kurth +2
We analytically determine the number and distribution of fixed points in a canonical model of a chaotic neural network. This distribution reveals that fixed points and dynamics are…
Origami in N dimensions: How feed-forward networks manufacture linear separability
Christian Keup, Moritz Helias
Neural networks can implement arbitrary functions. But, mechanistically, what are the tools at their disposal to construct the target? For classification tasks, the network must tr…
Decomposing neural networks as mappings of correlation functions
Kirsten Fischer, Alexandre René, Christian Keup +3
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 w…
Unfolding recurrence by Green's functions for optimized reservoir computing
Sandra Nestler, Christian Keup, David Dahmen +3
Cortical networks are strongly recurrent, and neurons have intrinsic temporal dynamics. This sets them apart from deep feed-forward networks. Despite the tremendous progress in the…
Transient chaotic dimensionality expansion by recurrent networks
Christian Keup, Tobias Kühn, David Dahmen +1
Neurons in the brain communicate with spikes, which are discrete events in time and value. Functional network models often employ rate units that are continuously coupled by analog…