Publications related to Comparison of dynamic load modeling using neural network and traditional method

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

EPFL Infoscience2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

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

Bed Topography Inference from Velocity Field Using Deep Learning.

Christophe Ancey, Mehrdad Kiani Oshtorjani

Measuring bathymetry has always been a major scientific and technological challenge. In this work, we used a deep learning technique for inferring bathymetry from the depth-averaged velocity field. The training of the neural network is based on 5742 labora ...

2023

Transformer Models for Vision

Jean-Baptiste Francis Marie Juliette Cordonnier

The recent developments of deep learning cover a wide variety of tasks such as image classification, text translation, playing go, and folding proteins.All these successful methods depend on a gradient-based learning algorithm to train a model on massive a ...

EPFL2023

Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset

Jiancheng Yang

Accurate segmentation of pulmonary airways and vessels is crucial for the diagnosis and treatment of pulmonary diseases. However, current deep learning approaches suffer from disconnectivity issues that hinder their clinical usefulness. To address this cha ...

Cham2023

An end-to-end pipeline for historical censuses processing

Lucas Arnaud André Rappo, Rémi Guillaume Petitpierre, Marion Kramer

Censuses are structured documents of great value for social and demographic history, which became widespread from the nineteenth century on. However, the plurality of formats and the natural variability of historical data make their extraction arduous and ...

2023

Lightning Nowcasting Using Solely Lightning Data

Marcos Rubinstein, Amirhossein Mostajabi

Lightning is directly or indirectly responsible for significant human casualties and property damage worldwide. A timely prediction of its occurrence can enable authorities and the public to take necessary precautionary actions resulting in diminishing the ...

Basel2023

CovNet: Covariance networks for functional data on multidimensional domains

Victor Panaretos, Soham Sarkar

Covariance estimation is ubiquitous in functional data analysis. Yet, the case of functional observations over multidimensional domains introduces computational and statistical challenges, rendering the standard methods effectively inapplicable. To address ...

WILEY2022

Controllability and Interpretability in Affective Speech Synthesis

Bastian Schnell

Thanks to Deep Learning Text-To-Speech (TTS) has achieved high audio quality with large databases. But at the same time the complex models lost any ability to control or interpret the generation process. For the big challenge of affective TTS it is infeasi ...

EPFL2022

Lightweight HI source finding for next generation radio surveys

Jean-Paul Richard Kneib, Frédéric Courbin, Aymeric Alexandre Galan, Austin Chandler Peel, Emma Elizabeth Tolley, Mark Thomas Sargent

Future deep HI surveys will be essential for understanding the nature of galaxies and the content of the Universe. However, the large volume of these data will require distributed and automated processing techniques. We introduce LiSA, a set of python modu ...

ELSEVIER2022

Comparison of dynamic load modeling using neural network and traditional method

Graph Chatbot

Chat with Graph Search

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Bed Topography Inference from Velocity Field Using Deep Learning.

Transformer Models for Vision

Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset

An end-to-end pipeline for historical censuses processing

Lightning Nowcasting Using Solely Lightning Data

CovNet: Covariance networks for functional data on multidimensional domains

Controllability and Interpretability in Affective Speech Synthesis

Lightweight HI source finding for next generation radio surveys

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Controllability and Interpretability in Affective Speech Synthesis

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

CovNet: Covariance networks for functional data on multidimensional domains

Transformer Models for Vision

Bed Topography Inference from Velocity Field Using Deep Learning.

Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset

Lightweight HI source finding for next generation radio surveys

Lightning Nowcasting Using Solely Lightning Data

An end-to-end pipeline for historical censuses processing