25 citations · 48 across the 7 of their papers we have counts for
7 papers
When Search Eats the Web: A Model of Corpus Erosion under Generative Extraction
Sylvain Peyronnet
Generative search engines (GSEs) answer user queries directly from crawled web content. The capture of value from the corpus without a visit returned to the source (we call this ca…
Graphcore C2 Card performance for image-based deep learning application: A Report
Ilyes Kacher, Maxime Portaz, Hicham Randrianarivo +1
Recently, Graphcore has introduced an IPU Processor for accelerating machine learning applications. The architecture of the processor has been designed to achieve state of the art…
Image search using multilingual texts: a cross-modal learning approach between image and text
Maxime Portaz, Hicham Randrianarivo, Adrien Nivaggioli +3
Multilingual (or cross-lingual) embeddings represent several languages in a unique vector space. Using a common embedding space enables for a shared semantic between words from dif…
Efficient filtering of adult content using textual information
Thomas Largillier, Guillaume Peyronnet, Sylvain Peyronnet
Nowadays adult content represents a non negligible proportion of the Web content. It is of the utmost importance to protect children from this content. Search engines, as an entry…
On the evaluation potential of quality functions in community detection for different contexts
Jean Creusefond, Thomas Largillier, Sylvain Peyronnet
Due to nowadays networks' sizes, the evaluation of a community detection algorithm can only be done using quality functions. These functions measure different networks/graphs struc…
Finding compact communities in large graphs
J. Creusefond, T. Largillier, S. Peyronnet
This article presents an efficient hierarchical clustering algorithm that solves the problem of core community detection. It is a variant of the standard community detection proble…
Uniform Random Sampling of Traces in Very Large Models
Alain Denise, Marie-Claude Gaudel, Sandrine-Dominique Gouraud +3
This paper presents some first results on how to perform uniform random walks (where every trace has the same probability to occur) in very large models. The models considered here…