Arthur Jacot

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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

Arthur Jacot

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

Neural Tangent Kernel: Convergence and Generalization in Neural Networks (Invited Paper)

Disentangling feature and lazy training in deep neural networks

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

Disentangling feature and lazy training in deep neural networks

Neural Tangent Kernel: Convergence and Generalization in Neural Networks (Invited Paper)