Publications associées à Théorie des réseaux

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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

Théorie des réseaux

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Feedback and Common Information: Bounds and Capacity for Gaussian Networks

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

Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data

Networks of Coupled VO2 Oscillators for Neuromorphic Computing

Leaderless Consensus

What can linearized neural networks actually say about generalization?

The Metabolic Regimes at the Scale of an Entire Stream Network Unveiled Through Sensor Data and Machine Learning

The physics of higher-order interactions in complex systems

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

True scale-free networks hidden by finite size effects

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

The Metabolic Regimes at the Scale of an Entire Stream Network Unveiled Through Sensor Data and Machine Learning

What can linearized neural networks actually say about generalization?

Feedback and Common Information: Bounds and Capacity for Gaussian Networks

Networks of Coupled VO2 Oscillators for Neuromorphic Computing

Leaderless Consensus

Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data

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