Publications associées à Annealing and Replica-Symmetry in Deep Boltzmann Machines

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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

Annealing and Replica-Symmetry in Deep Boltzmann Machines

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Predicting the long-term collective behaviour of fish pairs with deep learning

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems

Non-volatile data storage medium and system

Deep Boltzmann Machines: Rigorous Results at Arbitrary Depth

Local plasticity rules can learn deep representations using self-supervised contrastive predictions

Automation of the diagnosis process in the railway system : Detection of defects in concrete sleepers using vision-based machine learning models

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

Biologically plausible unsupervised learning in shallow and deep neural networks

Vision based pixel-level bridge structural damage detection using a link ASPP network

Private and Secure Distributed Learning

Local plasticity rules can learn deep representations using self-supervised contrastive predictions

Biologically plausible unsupervised learning in shallow and deep neural networks

Deep Boltzmann Machines: Rigorous Results at Arbitrary Depth

Automation of the diagnosis process in the railway system : Detection of defects in concrete sleepers using vision-based machine learning models

Predicting the long-term collective behaviour of fish pairs with deep learning

Non-volatile data storage medium and system

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

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems

Vision based pixel-level bridge structural damage detection using a link ASPP network

Private and Secure Distributed Learning