Publications related to Learning Network Structures from Firing Patterns

Deep Learning Theory Through the Lens of Diagonal Linear Networks

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Deep Learning Generalization with Limited and Noisy Labels

Mahsa Forouzesh

Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

EPFL2023

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Michaël Unser

The minimization of a data-fidelity term and an additive regularization functional gives rise to a powerful framework for supervised learning. In this paper, we present a unifying regularization functional that depends on an operator L\documentclass[12pt]{ ...

New York2023

Leveraging Unlabeled Data to Track Memorization

Patrick Thiran, Mahsa Forouzesh, Hanie Sedghi

Deep neural networks may easily memorize noisy labels present in real-world data, which degrades their ability to generalize. It is therefore important to track and evaluate the robustness of models against noisy label memorization. We propose a metric, ca ...

2023

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Elisabetta Cornacchia

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits.In the ...

EPFL2023

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Leonardo Petrini

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

EPFL2023

Privacy-preserving federated neural network training and inference

Sinem Sav

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...

EPFL2023

EpilepsyNet: Interpretable Self-Supervised Seizure Detection for Low-Power Wearable Systems

David Atienza Alonso, Amir Aminifar, Renato Zanetti

Epilepsy is one of the most common neurological disorders that is characterized by recurrent and unpredictable seizures. Wearable systems can be used to detect the onset of a seizure and notify family members and emergency units for rescue. The majority of ...

2023

An exact mapping from ReLU networks to spiking neural networks

Wulfram Gerstner, Stanislaw Andrzej Wozniak, Ana Stanojevic, Giovanni Cherubini, Angeliki Pantazi

Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an ...

2023

Exploring neural networks and their potential for species distribution modeling

Devis Tuia, Benjamin Alexander Kellenberger, Nina Marion Aurélia Van Tiel, Robin Adrien Zbinden, Lloyd Haydn Hughes

Species distribution models (SDMs) relate species occurrence data with environmental variables and are used to understand and predict species distributions across landscapes. While some machine learning models have been adopted by the SDM community, recent ...

2023

Learning Network Structures from Firing Patterns

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Deep Learning Generalization with Limited and Noisy Labels

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Leveraging Unlabeled Data to Track Memorization

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Privacy-preserving federated neural network training and inference

EpilepsyNet: Interpretable Self-Supervised Seizure Detection for Low-Power Wearable Systems

An exact mapping from ReLU networks to spiking neural networks

Exploring neural networks and their potential for species distribution modeling

Deep Learning Generalization with Limited and Noisy Labels

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Exploring neural networks and their potential for species distribution modeling

Leveraging Unlabeled Data to Track Memorization

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Privacy-preserving federated neural network training and inference

EpilepsyNet: Interpretable Self-Supervised Seizure Detection for Low-Power Wearable Systems

An exact mapping from ReLU networks to spiking neural networks