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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

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

stage 2020 : classification de gestes avec le radar Soli

EPIQUE: Exploring the Evolution of Science with Pivot Topic Graphs

NLP course at ESILV, 10/01/2019

An interactive exploration platform for topic evolution graphs

The code deposit for the "SSH Super-Resolution using high resolution SST with a Subpixel Convolutional Residual Network" article submitted at Climate informatics 2022

Imagenet classification using sparse patch codes.

Just a test with gitlab