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An unofficial LiP6 Beamer template for typesetting slides in LaTeX.

Algorithms to solve boolean polynomial systems

Hierarchical Models for Sentiment Analysis in Pytorch

NLP course at ESILV, 10/01/2019

ATEM is a novel framework for studying topic evolution in scientific archives. ATEM is based on dynamic topic modeling and dynamic graph embedding techniques that explore the dynamics of content and citations of documents within a scientific corpus. ATEM explores a new notion of contextual emergence for the discovery of emerging interdisciplinary research topics based on the dynamics of citation links in topic clusters. Our experiments show that ATEM can efficiently detect emerging cross-disciplinary topics within the DBLP archive of over five million computer science articles. paper: https://arxiv.org/abs/2306.02221

Test de protocol de consensus c4m

Control of accuracy and debugging for C/C++ and Fortran applications

Control of accuracy and debugging for CUDA applications

Control of accuracy and debugging for Python applications

Control of accuracy and debugging for C/C++ applications relying on fixed-point arithmetic

Control of accuracy and debugging for Fortran applications

Automatic transformation of C source codes for usage with Cadna