Publications associées à Neural networks : A tool for prediction of slope movements

Publications associées (12)

Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers

Transformers trained on natural language data have been shown to exhibit hierarchical generalization without explicitly encoding any structural bias. In this work, we investigate sources of inductive bias in transformer models and their training that could ...

MIT PRESS2025

Search for New Resonances Decaying to Pairs of Merged Diphotons in Proton-Proton Collisions at (Formula presented)

Jan Steggemann, Konstantin Androsov, Anna Mascellani, Joanna Malgorzata Wanczyk

A search is presented for an extended Higgs sector with two new particles, (Formula presented) and (Formula presented), in the process (Formula presented). Novel neural networks classify events with diphotons that are merged and determine the diphoton mass ...

American Physical Society (APS)2025

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Joshua Alexander Harrison Klein

The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

EPFL2024

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mattia Atzeni

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 ...

EPFL2024

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

Maude Girardin, Marco Picasso, Alexandre Caboussat

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 ...

Elsevier Science Sa2024

Estimation of horizontal running power using foot-worn inertial measurement units

Kamiar Aminian, Mathieu Pascal Falbriard, Salil Apte

Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this issue, we developed thr ...

FRONTIERS MEDIA SA2023

Sensitivity analysis in core diagnostics

Mathieu Hursin

In the CORTEX project, methods to simulate neutron flux oscillations were enhanced and machine-learning based tools to determine the causes of measured neutron flux oscillations were developed, using the results of simulations as training and validation da ...

2022

Discourse Phenomena in Machine Translation

Lesly Sadiht Miculicich Werlen

Machine Translation (MT) has made considerable progress in the past two decades, particularly after the introduction of neural network models (NMT). During this time, the research community has mostly focused on modeling and evaluating MT systems at the se ...

EPFL2021

Lupulus: A Flexible Hardware Accelerator For Neural Networks

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Robert Giterman, Andreas Toftegaard Kristensen

Neural networks have become indispensable for a wide range of applications, but they suffer from high computational- and memory-requirements, requiring optimizations from the algorithmic description of the network to the hardware implementation. Moreover, ...

IEEE2020

Wavefront shaping and deep learning in fiber endoscopy

Eirini Kakkava

Fiber endoscopy plays an important role in the clinical diagnosis and treatment processes involved in modern medicine. Thin fiber probes can relay information from confined places in the human body that are inaccessible for conventional bulky microscopes. ...

EPFL2020

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Michaël Unser, Michael Thompson McCann

This tutorial covers biomedical image reconstruction, from the foundational concepts of system modeling and direct reconstruction to modern sparsity and learning-based approaches. Imaging is a critical tool in biological research and medicine, and most ima ...

2019

Privacy-Sensitive Audio Features for Conversational Speech Processing

Sree Hari Krishnan Parthasarathi

The work described in this thesis takes place in the context of capturing real-life audio for the analysis of spontaneous social interactions. Towards this goal, we wish to capture conversational and ambient sounds using portable audio recorders. Analysis ...

EPFL2011