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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

Encode unicode strings in a silly way, based on glounicode

Implementing deep reinforcement learning algorithms for the ICGA competition.

The BoolEAN POLynomial Evaluation library

Cross domain dynamic

Bibliography related to the post-doc of Marek Felšöci