Publications related to Annealing and Replica-Symmetry in Deep Boltzmann Machines

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

Francesco Mondada, Alexandre Massoud Alahi, Vaios Papaspyros

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

2024

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

Indaco Biazzo

Autoregressive Neural Networks (ARNNs) have shown exceptional results in generation tasks across image, language, and scientific domains. Despite their success, ARNN architectures often operate as black boxes without a clear connection to underlying physic ...

Berlin2023

Non-volatile data storage medium and system

Martin Vetterli, Yves Bellouard, Ruben Ricca

A data storage medium (2) comprising a stacked plurality of layers (9), each layer composed of a layer material selected from a group comprising at least two different dielectric materials, adjacent layers being formed of different materials, and at least ...

2023

Deep Boltzmann Machines: Rigorous Results at Arbitrary Depth

Emanuele Mingione, Diego Alberici

A class of deep Boltzmann machines is considered in the simplified framework of a quenched system with Gaussian noise and independent entries. The quenched pressure of a K-layers spin glass model is studied allowing interactions only among consecutive laye ...

2021

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

Wulfram Gerstner, Bernd Albert Illing, Jean Ventura

Learning in the brain is poorly understood and learning rules that respect biological constraints, yet yield deep hierarchical representations, are still unknown. Here, we propose a learning rule that takes inspiration from neuroscience and recent advances ...

2021

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

Linah Charif

inspectors that walk over the track and check the defects on the rail surface, fasteners and sleepers. In the case of concrete sleepers, rail inspectors classify defects according to their size and occurrence over 20 sleepers. The manual inspection is erro ...

2021

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

Biologically plausible unsupervised learning in shallow and deep neural networks

Bernd Albert Illing

The way our brain learns to disentangle complex signals into unambiguous concepts is fascinating but remains largely unknown. There is evidence, however, that hierarchical neural representations play a key role in the cortex. This thesis investigates biolo ...

EPFL2021

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

Wenlong Deng

Structural Health Monitoring (SHM) has greatly benefited from computer vision. Recently, deep learning approaches are widely used to accurately estimate the state of deterioration of infrastructure. In this work, we focus on the problem of bridge surface s ...

2020

Private and Secure Distributed Learning

Georgios Damaskinos

The ever-growing number of edge devices (e.g., smartphones) and the exploding volume of sensitive data they produce, call for distributed machine learning techniques that are privacy-preserving. Given the increasing computing capabilities of modern edge de ...

EPFL2020

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

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

Biologically plausible unsupervised learning in shallow and deep neural networks

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

Non-volatile data storage medium and system

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

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

Deep Boltzmann Machines: Rigorous Results at Arbitrary Depth

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

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

Private and Secure Distributed Learning