Publications associées à Algorithme de compression sans perte

FEM of stress partition during compression of WC-10 wt.% Co with realistic 3D morphology

Daniele Mari, Lucas Eric Jacques Degeneve

A Finite Element Model is developed to represent the mechanical behavior of WC-10 wt.% Co in compression tests. In this research, realistic three-dimensional model is obtained by slicing a sample by Focused Ion Beam and reconstituting it, resulting in two ...

ELSEVIER SCI LTD2022

Volume change response and fabric evolution of granular MX80 bentonite along different hydro-mechanical stress paths

Alessio Ferrari, Jose Antonio Bosch Llufriu, Patrycja Ewelina Baryla

Despite the increasing understanding of bentonite behaviour, there is still missing evidence on how different hydro-mechanical loadings, including sequences of hydration and compression, affect the fabric and the volume change behaviour of the material. It ...

Springer Nature2022

Stochastic distributed learning with gradient quantization and double-variance reduction

Sebastian Urban Stich, Konstantin Mishchenko

We consider distributed optimization over several devices, each sending incremental model updates to a central server. This setting is considered, for instance, in federated learning. Various schemes have been designed to compress the model updates in orde ...

TAYLOR & FRANCIS LTD2022

Influence of Spatial Rendering on the Performance of Point Cloud Objective Quality Metrics

Touradj Ebrahimi, Michela Testolina

The use of point clouds to digitally represent three-dimensional objects with both geometry and color attributes is rapidly increasing in several applications. Since storage and transmission of uncompressed point cloud data are often impractical, several l ...

IEEE2022

Towards JPEG AIC part 3: visual quality assessment of high to visually lossless image coding

Touradj Ebrahimi, Evgeniy Upenik, Michela Testolina

Due to the increasing number of pictures captured and stored every day by and on digital devices, lossy image compression has become inevitable to limit the needed storage requirement. As a consequence, these compression methods might introduce some visual ...

SPIE2022

Structured and tiled-based pruning of Deep Learning models targeting FPGA implementations

Alexandre Schmid, Lizeth Gonzalez Carabarin

Model compression techniques have lead to a reduction of size and number of computations of Deep Learning models. However, techniques such as pruning mostly lack of a real co-optimization with hardware platforms. For instance, implementing unstructured pru ...

IEEE2022

Masked Training of Neural Networks with Partial Gradients

Martin Jaggi, Sebastian Urban Stich, Amirkeivan Mohtashami

State-of-the-art training algorithms for deep learning models are based on stochastic gradient descent (SGD). Recently, many variations have been explored: perturbing parameters for better accuracy (such as in Extra-gradient), limiting SGD updates to a sub ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression

Pascal Frossard, Thomas Maugey, Roberto Gerson De Albuquerque Azevedo

State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs). Although CNNs offer promising perspectives for 2D image compression, extending such models to omnidirectional images is not straightforward. First, omn ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

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Algorithme de compression sans perte

FEM of stress partition during compression of WC-10 wt.% Co with realistic 3D morphology

Latent Space Slicing for Enhanced Entropy Modeling in Learning-Based Point Cloud Geometry Compression

Volume change response and fabric evolution of granular MX80 bentonite along different hydro-mechanical stress paths

Stochastic distributed learning with gradient quantization and double-variance reduction

Integrated learning-based point cloud compression for geometry and color with graph Fourier transforms

Influence of Spatial Rendering on the Performance of Point Cloud Objective Quality Metrics

Towards JPEG AIC part 3: visual quality assessment of high to visually lossless image coding

Structured and tiled-based pruning of Deep Learning models targeting FPGA implementations

Masked Training of Neural Networks with Partial Gradients

OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression

Graph Chatbot

Chattez avec Graph Search

Volume change response and fabric evolution of granular MX80 bentonite along different hydro-mechanical stress paths

Integrated learning-based point cloud compression for geometry and color with graph Fourier transforms

Stochastic distributed learning with gradient quantization and double-variance reduction

Structured and tiled-based pruning of Deep Learning models targeting FPGA implementations

Influence of Spatial Rendering on the Performance of Point Cloud Objective Quality Metrics

FEM of stress partition during compression of WC-10 wt.% Co with realistic 3D morphology

Towards JPEG AIC part 3: visual quality assessment of high to visually lossless image coding

Latent Space Slicing for Enhanced Entropy Modeling in Learning-Based Point Cloud Geometry Compression

OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression

Masked Training of Neural Networks with Partial Gradients