Publications

Anna Willmann, Jurjen Couperus Cabadağ, Yen-Yu Chang, Richard Pausch, Amin Ghaith, Alexander Debus, Arie Irman, Michael Bussmann, Ulrich Schramm, und Nico Hoffmann. Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows. 2023. [PUMA: Bunch Distribution Electron Learning Zno imported] URL

Patrick Stiller, Varun Makdani, Franz Pöschel, Richard Pausch, Alexander Debus, Michael Bussmann, und Nico Hoffmann. Continual learning autoencoder training for a particle-in-cell simulation via streaming. 2022. [PUMA: Continual Zno autoencoder imported learning particle-in-cell simulation training] URL

Milad Alshomary, Timon Gurcke, Shahbaz Syed, Philipp Heinisch, Maximilian Spliethöver, Philipp Cimiano, Martin Potthast, und Henning Wachsmuth. Key Point Analysis via Contrastive Learning and Extractive Argument Summarization. In Khalid Al-Khatib, Yufang Hou, und Manfred Stede (Hrsg.), 8th Workshop on Argument Mining (ArgMining 2021) at EMNLP, 184--189, Association for Computational Linguistics, November 2021. [PUMA: Analysis Argument Contrastive Extractive Key Learning Point Summarization Zno]

Saeed Karami, Farid Saberi-Movahed, Prayag Tiwari, Pekka Marttinen, und 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

Markus Bauer, und Christoph Augenstein. Self-supervised learning in histopathology: New perspectives for prostate cancer grading. Lecture Notes in Computer Science, 348--360, Springer Nature Switzerland, Cham, 2024. [PUMA: Yaff cancer grading histopathology learning prostate self-supervised topic_lifescience]

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

Parvaneh Joharinad, und Jürgen Jost. Manifold learning, the scheme of Laplacian eigenmaps. Mathematics of Data, 227--251, Springer International Publishing, Cham, 2023. [PUMA: Laplacian Manifold eigenmaps learning nopdf]

Sascha Marton, Stefan Lüdtke, Christian Bartelt, und Heiner Stuckenschmidt. GradTree: Learning axis-aligned decision trees with gradient descent. 2023. [PUMA: GradTree Learning Zno axis-aligned decision descent gradient trees]

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

Ricardo Knauer, und Erik Rodner. Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective. KI 2023: Advances in Artificial Intelligence: 46th German Conference on AI, Berlin, Germany, September 26--29, 2023, Proceedings, 114--129, Springer-Verlag, Berlin, Heidelberg, 2023. [PUMA: Zno best conic cost-sensitive interpretable learning machine meta-learning mixed-integer optimization selection subset]

Dianzhao Li, und 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, Oktober 2024. [PUMA: Sim2Real Xack autonomous deep driving framework learning platform-agnostic reinforcement]

Sunna Torge, Waldemar Hahn, Lalith Manjunath, und 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

Sophie Adama, Shang-Ju Wu, Nicoletta Nicolaou, und Martin Bogdan. Extendable hybrid approach to detect conscious states in a CLIS patient using machine learning. SNE Simul. Notes Eur., (32)1:37--45, ARGESIM Arbeitsgemeinschaft Simulation News, 2022. [PUMA: Zno conscious hybrid learning machine patient states {CLIS}]

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]

Oliver Kirsten, Martin Bogdan, und Sophie Adama. Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient. 2023 7th International Conference on Imaging, Signal Processing and Communications (ICISPC), 37-41, 2023. [PUMA: Complexity Computational Consciousness Information Learning Locked-In Machine Measures Modeling Neuroscience Prediction Predictive Processing Signal Syndrome Theory Training Zno algorithms and data learning modeling models processing]

Maksim Kukushkin, Martin Bogdan, und Thomas Schmid. On optimizing morphological neural networks for hyperspectral image classification. In Wolfgang Osten (Hrsg.), 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, und 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, März 2024. [PUMA: BioDeepfuse RNA Zno classification deep extraction feature learning non-coding] URL

Meysam Alishahi, Anna Little, und Jeff M. Phillips. Linear Distance Metric Learning with Noisy Labels. Journal of Machine Learning Research, (25)121:1--53, 2024. [PUMA: Distance Learning Linear Metric Noisy_Labels Yaff imported] URL