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Control of accuracy and debugging for Python applications

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

Statistical mixture models with shared components

A repository for learning Poincaré ball representation for classification.

This is a tool intended to monitor the GPU usage on the various GPU-servers at the LIP6 Lab, Paris

PyTorch implementation of SPDNet for second order neural networks

Codes des exemples de l'article "Cadre déclaratif modulaire d'évaluation d'actions selon différents principes éthiques", pour la Revue d'Intelligence Artificielle

Algorithms to learn Atoms Network model based on learning subset of labels

Alliance/Coriolis Toolkit & Checker contain various design flow examples, blocks or full chips. It is also used as a regression test for all the tools.