Publications associées à Simplifying complex networks

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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

Simplifying complex networks

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

True scale-free networks hidden by finite size effects

NICEpath: Finding metabolic pathways in large networks through atom-conserving substrate-product pairs

The physics of higher-order interactions in complex systems

Learning Bollobas-Riordan Graphs Under Partial Observability

Consensus Control for Decentralized Deep Learning

Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing

Local Vulnerabilities and Global Robustness of Coupled Dynamical Systems on Complex Networks

Morphology and morphodynamics of braided rivers: an experimental investigation

Local Tomography of Large Networks Under the Low-Observability Regime

True scale-free networks hidden by finite size effects

The physics of higher-order interactions in complex systems

Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing

Consensus Control for Decentralized Deep Learning

Local Vulnerabilities and Global Robustness of Coupled Dynamical Systems on Complex Networks

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

NICEpath: Finding metabolic pathways in large networks through atom-conserving substrate-product pairs

Learning Bollobas-Riordan Graphs Under Partial Observability

Morphology and morphodynamics of braided rivers: an experimental investigation

Local Tomography of Large Networks Under the Low-Observability Regime