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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

A python library for multiprecision Affine Arithmetic

Python wrapper to load and read inrimage data

A tool to visualize 2D vector fields

Powerpoint and beamer template for MLIA/LIP6 using Sorbonne Université style

Just a test

Algorithms to solve boolean polynomial systems

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

An interactive exploration platform for topic evolution graphs

Short abstract