Publications

Saeed Karami, Farid Saberi-Movahed, Prayag Tiwari, Pekka Marttinen, and Sahar Vahdati. Unsupervised feature selection based on variance–covariance subspace distance. Neural Networks, (166):188-203, 2023. [PUMA: Feature Regularization Subspace Xack distance learning selection] URL

Akshay Akshay, Masoud Abedi, Navid Shekarchizadeh, Fiona C Burkhard, Mitali Katoch, Alex Bigger-Allen, Rosalyn M Adam, Katia Monastyrskaya, and Ali Hashemi Gheinani. MLcps: machine learning cumulative performance score for classification problems. GigaScience, (12):giad108, December 2023. [PUMA: MLcps Xack Yaff cumulative learning machine performance] URL

Niklas Deckers, and Martin Potthast. WARC-DL: Scalable Web Archive Processing for Deep Learning. 2022. [PUMA: Archive Deep Learning Processing Scalable WARC-DL Web Xack] URL

Dianzhao Li, and Ostap Okhrin. A platform-agnostic deep reinforcement learning framework for effective Sim2Real transfer towards autonomous driving. Commun Eng, (3)1:147, Springer Science and Business Media LLC, October 2024. [PUMA: Sim2Real Xack autonomous deep driving framework learning platform-agnostic reinforcement]

Sunna Torge, Waldemar Hahn, Lalith Manjunath, and René Jäkel. Named Entity Recognition for Specific Domains - Take Advantage of Transfer Learning. International Journal of Information Science and Technology, Vol 6 No 3 (2022), International Journal of Information Science and Technology, 2022. [PUMA: Advantage Domains Entity Learning Recognition Specific Transfer Xack] URL

Martin Bogdan. Learning algorithms for spiking neural networks: should one use learning algorithms from ANN/DL or neurological plausible learning? - A thought-provoking impulse. XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja), 201--207, Servizo de Publicacións da UDC, September 2022. [PUMA: Learning Xack algorithms learning networks neural neurological plausible spiking]

Lucas Lange, Maurice-Maximilian Heykeroth, and Erhard Rahm. Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning. arXiv preprint arXiv:2409.01329, arXiv, September 2024. [PUMA: (cs.CR), (cs.CV), (cs.LG), Computer Cryptography FOS: Learning Machine Pattern Recognition Security Vision and area_bigdata area_responsibleai ep information sciences xack yaff]

Vincent D. Friedrich, Peter Pennitz, Emanuel Wyler, Julia M. Adler, Dylan Postmus, Kristina Müller, Luiz Gustavo Teixeira Alves, Julia Prigann, Fabian Pott, Daria Vladimirova, Thomas Hoefler, Cengiz Goekeri, Markus Landthaler, Christine Goffinet, Antoine-Emmanuel Saliba, Markus Scholz, Martin Witzenrath, Jakob Trimpert, Holger Kirsten, and Geraldine Nouailles. Neural network-assisted humanisation of COVID-19 hamster transcriptomic data reveals matching severity states in human disease. eBioMedicine, (108):105312, 2024. [PUMA: COVID-19, Cross-species Deep Disease Hamster RNA-seq, Single-cell analysis, learning matching, model, state topic_mathfoundation xack yaff] URL

Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini, and Christian L. Müller. Stability selection enables robust learning of partial differential equations from limited noisy data. arXiv, 2019. [PUMA: (cs.LG), (math.NA), (physics.data-an), Analysis Analysis, Computer Data FOS: Learning Machine Mathematics, Numerical Physical Probability Statistics and information sciences sciences, xack] URL