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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

TRACFM : Next POI Recommendation using Mixed Transition and Collaborative Filtering Models

Just a test with gitlab

Special version of the M4RI library for the PPAR class.

Coriolis VLSI CAD Tools

Sources du cours Data Mining & Visualisation (M1 Master Management de l'Innovation - Sorbonne Université)

Example Hugo site using GitLab Pages: https://pages.gitlab.io/hugo

This repository provides a template to generate webpages for the team of Matthieu Cord.