Publications associées à Is the Network Turing-Complete?

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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

Is the Network Turing-Complete?

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Global, Passive Detection of Connection Tampering

Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment

Notes on Los Angeles

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

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

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

Efficient Max-Pressure Traffic Management for Large-Scale Congested Urban Networks

HolA: Holistic and Autonomous Attestation for IoT Networks

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

Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment

Global, Passive Detection of Connection Tampering

Notes on Los Angeles

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

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

Information Retrieval Under Network Uncertainty: Robust Internet Ranking

HolA: Holistic and Autonomous Attestation for IoT Networks

Efficient Max-Pressure Traffic Management for Large-Scale Congested Urban Networks

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