| 2026 | ESANN | Autoencoders versus PCA for feature extraction in FDG PET scans in neurodegenerative diseases. | Roland J. Veen, Sofie Lvdal, Kaitlin Vos, Ciro Setolino, Sanne K. Meles, Michael Biehl |
| 2025 | ESANN | Interpretable machine learning for the diagnosis of hyperkinetic movement disorders. | Elina L. van den Brandhof, Jan W. J. Elting, Inge Tuitert, A. M. Madelein van der Stouwe, Jelle R. Dalenberg, Marina A. J. Tijssen, Michael Biehl |
| 2025 | ESANN | The Role of the Learning Rate in Layered Neural Networks with ReLU Activation Function. | Otavio Citton, Frederieke Richert, Michael Biehl |
| 2025 | ESANN | Mitigating the Bias in Data for Fairness Using an Advanced Generalized Learning Vector Quantization Approach - FA(IR)$^2$MA-GLVQ. | Marika Kaden, Alexander Engelsberger, Ronny Schubert, Sofie Lvdal, Elina L. van den Brandhof, Michael Biehl, Thomas Villmann |
| 2024 | ESANN | On-line Learning Dynamics in Layered Neural Networks with Arbitrary Activation Functions. | Frederieke Richert, Otavio Citton, Michael Biehl |
| 2024 | ESANN | Interpreting Hybrid AI through Autodecoded Latent Space Entities. | Roland J. Veen, Christodoulos Hadjichristodoulou, Michael Biehl |
| 2024 | ICCS | A Graph-Theory Based fMRI Analysis. | Luca Barillaro, Marianna Milano, Maria Eugenia Caligiuri, Jelle R. Dalenberg, Giuseppe Agapito, Michael Biehl, Mario Cannataro |
| 2023 | ESANN | Improved Interpretation of Feature Relevances: Iterated Relevance Matrix Analysis (IRMA). | Michael Biehl, Sofie Lvdal |
| 2023 | ESANN | Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis. | Frederieke Richert, Michiel Straat, Elisa Oostwal, Michael Biehl |
| 2022 | IDA | A Learning Vector Quantization Architecture for Transfer Learning Based Classification in Case of Multiple Sources by Means of Null-Space Evaluation. | Thomas Villmann, Daniel Staps, Jensun Ravichandran, Sascha Saralajew, Michael Biehl, Marika Kaden |
| 2020 | ICPRAM | Structure Preserving Encoding of Non-euclidean Similarity Data. | Maximilian Mnch, Christoph Raab, Michael Biehl, Frank-Michael Schleif |
| 2020 | SSPR | Complex-Valued Embeddings of Generic Proximity Data. | Maximilian Mnch, Michiel Straat, Michael Biehl, Frank-Michael Schleif |
| 2019 | CAIP | A Computer Vision Pipeline that Uses Thermal and RGB Images for the Recognition of Holstein Cattle. | Amey Bhole, Owen Falzon, Michael Biehl, George Azzopardi |
| 2019 | ESANN | Statistical physics of learning and inference. | Michael Biehl, Nestor Caticha, Manfred Opper, Thomas Villmann |
| 2019 | ESANN | Feature relevance bounds for ordinal regression. | Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tio, Barbara Hammer |
| 2019 | ESANN | On-line learning dynamics of ReLU neural networks using statistical physics techniques. | Michiel Straat, Michael Biehl |
| 2018 | ESANN | Machine learning and data analysis in astroinformatics. | Michael Biehl, Kerstin Bunte, Giuseppe Longo, Peter Tio |
| 2018 | ESANN | Prototype-based analysis of GAMA galaxy catalogue data. | Aleke Nolte, Lingyu Wang, Michael Biehl |
| 2017 | ESANN | Biomedical data analysis in translational research: integration of expert knowledge and interpretable models. | Gyan Bhanot, Michael Biehl, Thomas Villmann, Dietlind Zhlke |
| 2017 | ESANN | Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders. | Sreejita Ghosh, Elizabeth Sarah Baranowski, Rick van Veen, Gert-Jan de Vries, Michael Biehl, Wiebke Arlt, Peter Tio, Kerstin Bunte |
| 2017 | ICAISC | Sequence Learning in Unsupervised and Supervised Vector Quantization Using Hankel Matrices. | Mohammad Mohammadi, Michael Biehl, Andrea Villmann, Thomas Villmann |
| 2017 | IJCNN | Marker selection for the detection of trisomy 21 using generalized matrix learning vector quantization. | Andreas C. Neocleous, Costas Neocleous, Christos N. Schizas, Michael Biehl, Nicolai Petkov |
| 2016 | CEC | Predicting recurrence in clear cell Renal Cell Carcinoma: Analysis of TCGA data using outlier analysis and generalized matrix LVQ. | Gargi Mukherjee, Gyan Bhanot, Kevin Raines, Srikanth Sastry, Sebastian Doniach, Michael Biehl |
| 2015 | CAIP | Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. | Thomas Villmann, Marika Kaden, David Nebel, Michael Biehl |
| 2015 | CAIP | Facial Expression Recognition Using Learning Vector Quantization. | Gert-Jan de Vries, Steffen Pauws, Michael Biehl |
| 2015 | ESANN | Combining dissimilarity measures for prototype-based classification. | Ernest Mwebaze, Gjalt Bearda, Michael Biehl, Dietlind Zhlke |
| 2015 | IJCNN | Stationarity of Matrix Relevance LVQ. | Michael Biehl, Barbara Hammer, Frank-Michael Schleif, Petra Schneider, Thomas Villmann |
| 2014 | CIDM | Valid interpretation of feature relevance for linear data mappings. | Benot Frnay, Daniela Hofmann, Alexander Schulz, Michael Biehl, Barbara Hammer |
| 2014 | ESANN | Segmented shape-symbolic time series representation. | Herbert Teun Kruitbosch, Ioannis Giotis, Michael Biehl |
| 2013 | CIDM | Regularization and improved interpretation of linear data mappings and adaptive distance measures. | Marc Strickert, Barbara Hammer, Thomas Villmann, Michael Biehl |
| 2013 | ESANN | Non-Euclidean independent component analysis and Oja's learning. | Mandy Lange, Michael Biehl, Thomas Villmann |
| 2012 | ESANN | Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors. | Michael Biehl, Petra Schneider, David Smith, Han Stiekema, Angela Taylor, Beverly Hughes, Cedric Shackleton, Paul Stewart, Wiebke Arlt |
| 2012 | ESANN | Adaptive learning for complex-valued data. | Kerstin Bunte, Frank-Michael Schleif, Michael Biehl |
| 2012 | ESANN | Visualizing the quality of dimensionality reduction. | Bassam Mokbel, Wouter Lueks, Andrej Gisbrecht, Michael Biehl, Barbara Hammer |
| 2012 | ICMLA | Differentiable Kernels in Generalized Matrix Learning Vector Quantization. | Marika Kstner, David Nebel, Martin Riedel, Michael Biehl, Thomas Villmann |
| 2012 | IJCNN | Large margin linear discriminative visualization by Matrix Relevance Learning. | Michael Biehl, Kerstin Bunte, Frank-Michael Schleif, Petra Schneider, Thomas Villmann |
| 2012 | PST | Visualization of processes in self-learning systems. | Gabriele Peters, Kerstin Bunte, Marc Strickert, Michael Biehl, Thomas Villmann |
| 2011 | CAIP | Adaptive Matrices for Color Texture Classification. | Kerstin Bunte, Ioannis Giotis, Nicolai Petkov, Michael Biehl |
| 2011 | CIDM | Dimensionality reduction mappings. | Kerstin Bunte, Michael Biehl, Barbara Hammer |
| 2011 | ESANN | Supervised dimension reduction mappings. | Kerstin Bunte, Michael Biehl, Barbara Hammer |
| 2011 | ESANN | Generalized functional relevance learning vector quantization. | Marika Kstner, Barbara Hammer, Michael Biehl, Thomas Villmann |
| 2011 | ESANN | Causal relevance learning for robust classification under interventions. | Ernest Mwebaze, John A. Quinn, Michael Biehl |
| 2011 | ESANN | Learning of causal relations. | John A. Quinn, Joris M. Mooij, Tom Heskes, Michael Biehl |
| 2011 | ESANN | Multivariate class labeling in Robust Soft LVQ. | Petra Schneider, Tina Geweniger, Frank-Michael Schleif, Michael Biehl, Thomas Villmann |
| 2010 | ALIFE | Early Nervous Systems - Theoretical Background and a Preliminary Model of Neuronal Processes. | Ot de Wiljes, Ronald A. J. van Elburg, Michael Biehl, Fred Keijzer |
| 2010 | ESANN | Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization. | Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismller |
| 2010 | ESANN | Divergence based Learning Vector Quantization. | Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Sven Haase, Thomas Villmann, Michael Biehl |
| 2010 | IDEAL | Generalized Derivative Based Kernelized Learning Vector Quantization. | Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider, Michael Biehl |
| 2009 | CAIP | Nonlinear Dimension Reduction and Visualization of Labeled Data. | Kerstin Bunte, Barbara Hammer, Michael Biehl |
| 2009 | ESANN | Adaptive Metrics for Content Based Image Retrieval in Dermatology. | Kerstin Bunte, Michael Biehl, Nicolai Petkov, Marcel F. Jonkman |
| 2009 | ESANN | Nonlinear Discriminative Data Visualization. | Kerstin Bunte, Barbara Hammer, Petra Schneider, Michael Biehl |
| 2009 | ESANN | Hyperparameter Learning in Robust Soft LVQ. | Petra Schneider, Michael Biehl, Barbara Hammer |
| 2009 | ESANN | Equilibrium properties of off-line LVQ. | Aree Witoelar, Michael Biehl, Barbara Hammer |
| 2009 | IWANN | Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data. | Marc Strickert, Jens Keilwagen, Frank-Michael Schleif, Thomas Villmann, Michael Biehl |
| 2008 | ESANN | Generalized matrix learning vector quantizer for the analysis of spectral data. | Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl |
| 2008 | ESANN | Phase transitions in Vector Quantization. | Aree Witoelar, Anarta Ghosh, Michael Biehl |
| 2007 | ESANN | Relevance matrices in LVQ. | Petra Schneider, Michael Biehl, Barbara Hammer |
| 2007 | ESANN | On the dynamics of Vector Quantization and Neural Gas. | Aree Witoelar, Michael Biehl, Anarta Ghosh, Barbara Hammer |
| 2007 | IDEAL | Analysis of Tiling Microarray Data by Learning Vector Quantization and Relevance Learning. | Michael Biehl, Rainer Breitling, Yang Li |
| 2006 | ESANN | Classification of Boar Sperm Head Images using Learning Vector Quantization. | Michael Biehl, Piter Pasma, Marten Pijl, Lidia Snchez, Nicolai Petkov |
| 2005 | ESANN | The dynamics of Learning Vector Quantization. | Michael Biehl, Anarta Ghosh, Barbara Hammer |
| 2002 | ESANN | Supervised learning in committee machines by PCA. | Christoph Bunzmann, Michael Biehl, Robert Urbanczik |