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Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

Wulfram Gerstner, Clément Hongler, Johanni Michael Brea, Francesco Spadaro, Berfin Simsek, Arthur Jacot

We study how permutation symmetries in overparameterized multi-layer neural networks generate `symmetry-induced' critical points. Assuming a network with

L

layers of minimal widths

r_1^*, \ldots, r_{L-1}^*

reaches a zero-loss minimum at $ r_1^*! \c ...

2021

Risk Perception and Epidemics in Complex Computer Networks

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The kinectome: A comprehensive kinematic map of human motion in health and disease

Optimizing the mitigation of epidemic spreading through targeted adoption of contact tracing apps

Supplementary datasets for "ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds"

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

An automatized signal timing detection at signalized intersections with detailed vehicle trajectory data

Epidemic mitigation by statistical inference from contact tracing data

Morphology and morphodynamics of braided rivers: an experimental investigation

Who Is Listening? Spokesperson Effect on Communicating Social and Physical Distancing Measures During the COVID-19 Pandemic

Social Learning Under Inferential Attacks

Switchover phenomenon induced by epidemic seeding on geometric networks