Coarse-to-fine textures retrieval in the JPEG 2000 compressed domain for fast browsing of large image databases

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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 ...

Codebook-based optimizations are a class of algorithmic-level transformations able to effectively reduce the computing and memory requirements of Convolutional Neural Networks (CNNs). This approach tightly limits the number of unique weights in each layer, ...

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 ...

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 ...

The growing adoption of point clouds as an imaging modality has stimulated the search for efficient solutions for compression. Learning-based algorithms have been reporting increasingly better performance and are drawing the attention from the research com ...

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 ...

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 ...

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 ...

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 ...

Coarse-to-fine textures retrieval in the JPEG 2000 compressed domain for fast browsing of large image databases

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

An Accuracy-Driven Compression Methodology to Derive Efficient Codebook-Based CNNs

Stochastic distributed learning with gradient quantization and double-variance reduction

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

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

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

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