Learning a Convex Patch-Based Synthesis Model via Deep Equilibrium

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How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model

Matthieu Wyart, Leonardo Petrini, Alessandro Favero, Francesco Cagnetta, Umberto Maria Tomasini

Deep learning algorithms demonstrate a surprising ability to learn high-dimensional tasks from limited examples. This is commonly attributed to the depth of neural networks, enabling them to build a hierarchy of abstract, low-dimensional data representatio ...

American Physical Society2024

Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks

Ana-Maria Cretu

Smart meters, devices measuring the electricity and gas consumption of a household, are currently being deployed at a fast rate throughout the world. The data they collect are extremely useful, including in the fight against climate change. However, these ...

Association for Computing Machinery, Inc2024

Prediction rigidities for data-driven chemistry

Michele Ceriotti, Federico Grasselli, Philip Robin Loche, Matthias Linus Kellner, Sanggyu Chong, Filippo Bigi

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency w ...

2024

Distributed Adaptive Learning Under Communication Constraints

Ali H. Sayed

We consider a network of agents that must solve an online optimization problem from continual observation of streaming data. To this end, the agents implement a distributed cooperative strategy where each agent is allowed to perform local exchange of infor ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Learning to Boost the Performance of Stable Nonlinear Systems

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti

The growing scale and complexity of safety-critical control systems underscore the need to evolve current control architectures aiming for the unparalleled performances achievable through state-of-the-art optimization and machine learning algorithms. Howev ...

2024

Leveraging Unlabeled Data to Track Memorization

Patrick Thiran, Mahsa Forouzesh

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

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

Olga Fink, Gaëtan Michel Frusque

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, di ...

2023

Decoding cerebro-spinal signatures of human behavior: Application to motor sequence learning

Dimitri Nestor Alice Van De Ville, Nawal Noëlle Kinany

Mapping the neural patterns that drive human behavior is a key challenge in neuroscience. Even the simplest of our everyday actions stem from the dynamic and complex interplay of multiple neural structures across the central nervous system (CNS). Yet, most ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2023

Benefits of Max Pooling in Neural Networks: Theoretical and Experimental Evidence

François Fleuret, Kyle Michael Matoba, Nikolaos Dimitriadis

When deep neural networks became state of the art image classifiers, numerous max pooling operations were an important component of the architecture. However, modern computer vision networks typically have few, if any, max pooling operations. To understand ...

Journal of Machine Learning Research Inc.2023

Phase diagram of stochastic gradient descent in high-dimensional two-layer neural networks

Lenka Zdeborová, Florent Gérard Krzakala, Ludovic Théo Stephan, Rodrigo Soares Veiga

Despite the non-convex optimization landscape, over-parametrized shallow networks are able to achieve global convergence under gradient descent. The picture can be radically different for narrow networks, which tend to get stuck in badly-generalizing local ...

Iop Publishing Ltd2023

NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

Willem Anna Mark Wybo, Bernd Albert Illing

While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contex-tual sta ...

National Academy of Sciences2023