Using an Artificial Neural Network to Determine Electrical Properties of Epithelia. - Thomas Schmid 003; Dorothee Günzel; Martin Bogdan (ICANN (1), 2010)

Efficient prediction of x-axis intercepts of discrete impedance spectra. - Thomas Schmid 003; Dorothee Günzel; Martin Bogdan (ESANN, 2013)

Automated Quantification of the Relation between Resistor-capacitor Subcircuits from an Impedance Spectrum. - Thomas Schmid 003; Dorothee Günzel; Martin Bogdan (BIOSIGNALS, 2014)

Macht "Big Data" synthetische Datensätze überflüssig? - Thomas Schmid 003 (Informatiktage, 2014)

Deconstructing the Final Frontier of Artificial Intelligence: Five Theses for a Constructivist Machine Learning. - Thomas Schmid 003 (AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering, 2019)

Using Learning Algorithms to Create, Exploit and Maintain Knowledge Bases: Principles of Constructivist Machine Learning. - Thomas Schmid 003 (AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering (1), 2020)

Batch-like Online Learning for More Robust Hybrid Artificial Intelligence: Deconstruction as a Machine Learning Process. - Thomas Schmid 003 (AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering, 2021)

The AI Methods, Capabilities and Criticality Grid. - Thomas Schmid 003; Wolfgang Hildesheim; Taras Holoyad; Kinga Schumacher (Künstliche Intell., 2021)

Improved Techniques for Training Tabular GANs Using Cramer's V Statistics. - Melle Mendikowski; Benjamin Schindler; Thomas Schmid 003; Ralf Möller 001; Mattis Hartwig (Canadian AI, 2023)

Canola seed or not? Autoencoder-based Anomaly Detection in AgriculturalSeedProduction. - Maksim Kukushkin; Matthias Enders; Reinhard Kaschuba; Martin Bogdan; Thomas Schmid 003 (GI-Jahrestagung, 2023)

On optimizing morphological neural networks for hyperspectral image classification. - Maksim Kukushkin; Martin Bogdan; Thomas Schmid 003 (ICMV, 2023)

Constructivist Machine Learning. - Thomas Schmid 003 (Compendium of Neurosymbolic Artificial Intelligence, 2023)

Machine learning in AI Factories - five theses for developing, managing and maintaining data-driven artificial intelligence at large scale. - Wolfgang Hildesheim; Taras Holoyad; Thomas Schmid 003 (it Inf. Technol., 2023)

BiMAE - A Bimodal Masked Autoencoder Architecture for Single-Label Hyperspectral Image Classification. - Maksim Kukushkin; Martin Bogdan; Thomas Schmid 003 (CVPR Workshops, 2024)

Physics-Aware Normalizing Flows: Leveraging Electric Circuit Models in Adversarial Learning. - Benjamin Schindler; Thomas Schmid 003 (ESANN, 2024)

BiCAE - A Bimodal Convolutional Autoencoder for Seed Purity Testing. - Maksim Kukushkin; Martin Bogdan; Thomas Schmid 003 (ECML/PKDD (10), 2024)

The PanAf-FGBG Dataset: Understanding the Impact of Backgrounds in Wildlife Behaviour Recognition. - Otto Brookes; Maksim Kukushkin; Majid Mirmehdi; Colleen Stephens; Paula Dieguez; Thurston C. Hicks; Sorrel Jones; Kevin Lee; Maureen S. McCarthy; Amelia Meier; Emmanuelle Normand; Erin G. Wessling; et al. (CVPR, 2025)

Deep dive into KABR: a dataset for understanding ungulate behavior from in-situ drone video. - Maksim Kholiavchenko; Jenna Kline; Maksim Kukushkin; Otto Brookes; Sam Stevens 001; Isla Duporge; Alec Sheets; Reshma Ramesh Babu; Namrata Banerji; Elizabeth G. Campolongo; Matthew Thompson; Nina Van Tiel; et al. (Multim. Tools Appl., 2025)