Publications related to Determining 1D fast-ion velocity distribution functions from ion cyclotron emission data using deep neural networks

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

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

EPFL2024

Generalization 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 ...

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Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

2024

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Jean-Paul Richard Kneib, Emma Elizabeth Tolley, Tianyue Chen, Michele Bianco

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Oxford Univ Press2024

Coupling 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, ...

Berlin2024

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Marco Picasso, Alexandre Caboussat, Maude Girardin

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

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Optimizing Dynamic Aperture Studies with Active Learning

Davide Di Croce, Tatiana Pieloni, Ekaterina Krymova, Massimo Giovannozzi

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

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M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

David Atienza Alonso, Amir Aminifar, Tomas Teijeiro Campo, Alireza Amirshahi, Farnaz Forooghifar, Saleh Baghersalimi

Integrating low-power wearable systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables make it possible to target complex scenarios by exploiting multiple biosignals and using high-perfor ...

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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

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

Determining 1D fast-ion velocity distribution functions from ion cyclotron emission data using deep neural networks

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Optimizing Dynamic Aperture Studies with Active Learning

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

Deep Learning Generalization with Limited and Noisy Labels

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

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Optimizing Dynamic Aperture Studies with Active Learning

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems