Category
Artificial neural networks
Related publications (1,000)
Safe Deep Neural Networks
The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is tr ...
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 -layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...
EPFL2024Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning
The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...
EPFL2024Topics in statistical physics of high-dimensional machine learning
In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...
EPFL2024On the number of regions of piecewise linear neural networks
Michaël Unser, Alexis Marie Frederic Goujon
Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is affine. The number of these so-called linear regions offers a natural metric ...
2024Random matrix methods for high-dimensional machine learning models
In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...
EPFL2024Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging
Edoardo Charbon, Claudio Bruschini, Andrei Ardelean, Paul Mos, Yang Lin
Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...
Berlin2024Generalization of Scaled Deep ResNets in the Mean-Field Regime
Volkan Cevher, Grigorios Chrysos, Fanghui Liu
Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...
2024Understanding generalization and robustness in modern deep learning
In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...
EPFL2024A graph convolutional autoencoder approach to model order reduction for parametrized PDEs
Jan Sickmann Hesthaven, Federico Pichi
The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...
San Diego2024