19 citations · 45 across the 5 of their papers we have counts for
6 papers
Teacher Geometry Shapes Learnability in Teacher-Student Networks
Kai J. Sandbrink, Flavio Martinelli, Alexander van Meegen +2
Teacher-student systems, in which a teacher neural network generates training labels so that a student neural network can learn to implement the same function, are widely used as a…
Flat Channels to Infinity in Neural Loss Landscapes
Flavio Martinelli, Alexander Van Meegen, Berfin Şimşek +2
The loss landscapes of neural networks contain minima and saddle points that may be connected in flat regions or appear in isolation. We identify and characterize a special structu…
Coding schemes in neural networks learning classification tasks
Alexander van Meegen, Haim Sompolinsky
Neural networks posses the crucial ability to generate meaningful representations of task-dependent features. Indeed, with appropriate scaling, supervised learning in neural networ…
Linking Network and Neuron-level Correlations by Renormalized Field Theory
Michael Dick, Alexander van Meegen, Moritz Helias
It is frequently hypothesized that cortical networks operate close to a critical point. Advantages of criticality include rich dynamics well-suited for computation and critical slo…
Unified field theoretical approach to deep and recurrent neuronal networks
Kai Segadlo, Bastian Epping, Alexander van Meegen +3
Understanding capabilities and limitations of different network architectures is of fundamental importance to machine learning. Bayesian inference on Gaussian processes has proven…
Large Deviations Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions
Alexander van Meegen, Tobias Kühn, Moritz Helias
We here unify the field theoretical approach to neuronal networks with large deviations theory. For a prototypical random recurrent network model with continuous-valued units, we s…