Two machine learning papers by BIFOLD researchers received the “Essential Science indicators” (ESI) “Highly Cited” and “Hot Papers” labels for their impact in the science community.
The paper “A Unifying Review of Deep and Shallow Anomaly Detection,” authored by Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, and Klaus-Robert Müller was ranked as “Highly Cited”. This means it was among the top one percent of most cited papers in the subject area of “Engineering”.
Additionally, the paper “Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data,” authored by Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek was marked as a “Hot Paper”. Hot Papers are papers published in the last two years that are receiving citations quickly after publication. These papers have been cited enough times in the most recent bimonthly period to place them in the top 0.1% when compared to papers in the same field and added to the database in the same period.
More information is available at Fraunhofer HHI.