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Implementing deep reinforcement learning algorithms for the ICGA competition.

ATEM is a novel framework for studying topic evolution in scientific archives. It 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.

A blockgraph for mesh networks

Test de protocol de consensus c4m

Cross domain dynamic

A decentralized learning research framework

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

INRIMAGE is a software dedicated to image processing

Additional commands and libraries for inrimage distribution

Python wrapper to load and read inrimage data