Publications

Maksim Kukushkin, Martin Bogdan, and Thomas Schmid. On optimizing morphological neural networks for hyperspectral image classification. In Wolfgang Osten (Eds.), Sixteenth International Conference on Machine Vision (ICMV 2023), (13072):1307202, SPIE, 2024. [PUMA: classification computer deep hyperspectral image learning mathematical morphological morphology networks neuronal nopdf remote sensing vision] URL

Anderson P. Avila Santos, Breno L. S. de Almeida, Robson P. Bonidia, Peter F. Stadler, Polonca Stefanic, Ines Mandic-Mulec, Ulisses Rocha, Danilo S. Sanches, and André C.P.L.F. de Carvalho. BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification. RNA Biology, (21)1:410–421, Informa UK Limited, March 2024. [PUMA: BioDeepfuse RNA classification deep extraction feature learning non-coding zno] URL

Lena Jurkschat, Gregor Wiedemann, Maximilian Heinrich, Mattes Ruckdeschel, and Sunna Torge. Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate. In Nicoletta Calzolari, Frederic Bechet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Helene Mazo, Jan Odijk, and Stelios Piperidis (Eds.), 2022 Language Resources and Evaluation Conference, LREC 2022, 663--672, European Language Resources Association (ELRA), 2022. [PUMA: FIS_scads argument aspect-based aspects, classification discourse, energy few-shot frames, mining, nuclear text xack learning]

Alexander Kurz, Katja Hauser, Hendrik Alexander Mehrtens, Eva Krieghoff-Henning, Achim Hekler, Jakob Nikolas Kather, Stefan Fröhling, Christof von Kalle, and Titus Josef Brinker. Uncertainty estimation in medical image classification: Systematic review. JMIR Med. Inform., (10)8:e36427, August 2022. [PUMA: calibration classification deep_detection estimation image imaging learning medical network nopdf out-of-distribution topic_lifescience uncertainty]

Christopher Klapproth, Rituparno Sen, Peter F Stadler, Sven Findeiß, and Jörg Fallmann. Common features in lncRNA annotation and classification: A survey. Noncoding RNA, (7)4:77, MDPI AG, December 2021. [PUMA: classification coding extraction feature learning lncRNA machine problems sequence yaff]

Akshay Akshay, Mitali Katoch, Navid Shekarchizadeh, Masoud Abedi, Ankush Sharma, Fiona C Burkhard, Rosalyn M Adam, Katia Monastyrskaya, and Ali Hashemi Gheinani. Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis. Gigascience, (13)January 2024. [PUMA: AutoML analysis classification data learning machine problems topic_federatedlearn visualization xack yaff]

Mariia Tkachenko, Claire Chalopin, Boris Jansen-Winkeln, Thomas Neumuth, Ines Gockel, and Marianne Maktabi. Impact of pre- and post-processing steps for supervised classification of colorectal cancer in hyperspectral images. Cancers (Basel), (15)7April 2023. [PUMA: cancer classification colorectal convolutional filter hyperspectral imaging learning machine median networks post-processing pre-processing topic_lifescience yaff]