Reconstruction from Spatio-Spectrally Coded Multispectral Light Fields.
Schambach, M.
PhD Thesis, Institute of Industrial Information Technology, Karlsruhe Institute of Technology, 2022.
[thesis] [code]
Quantization of the Proca field in curved spacetimes – A study of mass dependence and the zero mass limit.
Schambach, M.
MSc Thesis, Institute for Theoretical Physics, Leipzig University, 2016.
[thesis]
Graphical expansion of the partition function for the supersymmetric non-linear σ-model in (1+0) D.
Schambach, M.
BSc Thesis, Institute for Theoretical Physics, University of Jena, 2013.
[thesis]

Progressive Updates of Convolutional Neural Networks for Enhanced Reliability in Small Satellite Applications.
Kondrateva, O., Dietzel, S., Schambach, M., Otterbach, S., and Scheuermann, B.
Computer Communications, 2024.
[paper]
A Compact Multispectral Light Field Camera Based on an Inkjet-printed Microlens Array and Color Filter Array.
Zhang, Q., Schambach, M., Jin, Q., Heizmann, M., and Lemmer, U.
Optics Express, 2024.
[paper]
Explainability and Interpretability in Electric Load Forecasting Using Machine Learning Techniques – A Review.
Baur, L., Ditschuneit, K., Schambach, M., Kaymakci, C., Wollmann, T., and Sauer, A.
Energy and AI, 2024.
[paper]
Towards Tabular Foundation Models: Status Quo, Challenges, and Opportunities.
Schambach, M.
White paper. Merantix Momentum. (hal-04440710), 2024.
[paper]
Fabrication of Microlens Arrays with High Quality and High Fill Factor by Inkjet Printing.
Zhang, Q., Schambach, M., Schlisske, S., Jin, Q., Mertens, A., Hernandez-Sosa, G., Heizmann, M., and Lemmer, U.
Advanced Optical Materials, 2022.
[paper]
Automated Quality Assessment of Inkjet-Printed Microlens Arrays.
Schambach, M., Zhang, Q., Lemmer, U., and Heizmann, M.
tm – Technisches Messen, 88.6, pp.342–351, 2021.
[paper]
A Multispectral Light Field Dataset and Framework for Light Field Deep Learning.
Schambach, M. and Heizmann, M.
IEEE Access, 8, pp.193492–193502, 2020.
[paper] [code] [data]
Microlens Array Grid Estimation, Light Field Decoding, and Calibration.
Schambach, M. and Puente Léon, F.
IEEE Transactions on Computational Imaging, 6, pp.591–603, 2020.
[paper] [code] [data]
Reconstruction of Multispectral Images from Spectrally Coded Light Fields of Flat Scenes.
Schambach, M. and Puente León, F.
tm – Technisches Messen, 86.12, pp.758–764, 2019.
[paper] [code]
The Proca Field in Curved Spacetimes and its Zero Mass Limit.
Schambach, M. and Sanders, K.
Reports on Mathematical Physics, 82.2, pp.203–239, 2018.
[paper]

ConTextTab: A Semantics-Aware Tabular In-Context Learner.
Spinaci, M., Polewczyk,, M., and Schambach, M., and Thelin, S.
arXiv:2506.10707, 2025.
[paper]
Scaling Experiments in Self-Supervised Cross-Table Representation Learning.
Schambach, M., Paul, D., and Otterbach, J.
NeurIPS Table Representation Learning Workshop, 2023.
[paper]
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models.
Siems, J., Ditschuneit, K., Ripken, W., Lindborg, A., Schambach, M., Otterbach, J., and Genzel, M.
Advances in Neural Information Processing System (NeurIPS), 2023.
[paper]
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models.
Siems, J., Ditschuneit, K., Ripken, W., Lindborg, A., Schambach, M., Otterbach, J., and Genzel, M.
ICML 3rd Workshop on Interpretable Machine Learning in Healthcare, 2023.
[paper]
Filling the Gap: Fault-Tolerant Updates of On-Satellite Neural Networks Using Vector Quantization.
Kondrateva, O., Dietzel, S., Schambach, M., Otterbach, J., and Scheuermann, B.
IFIP Networking, 2023.
Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation.
Koenig, A., Schambach, M., and Otterbach, J.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023.
[paper]
Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management.
Siems, J., Schambach, M., Schulze, S., and Otterbach, J.
ICLR AI for Agent-Based Modelling Workshop, 2023.
[paper]
Spectral Reconstruction and Disparity from Spatio-Spectrally Coded Light Fields via Multi-Task Deep Learning.
Schambach, M., Shi, J., and Heizmann, M.
International Conference on 3D Vision (3DV), 2021.
[paper] [data]
Improving Light Efficiency in Multispectral Imaging via Complementary Notch Filters.
Panther,T.*, Schambach, M.*, and Heizmann, M.
Automated Visual Inspection and Machine Vision IV
International Society for Optics and Photonics (SPIE), 2021.
[paper]
Automated Quantitative Quality Assessment of Printed Microlens Arrays.
Schambach, M., Zhang, Q., Lemmer, U., and Puente León, F.
Forum Bildverarbeitung, KIT Scientific Publishing, 2021.
[paper]
Signal-Adapted Analytic Wavelet Packets in Arbitrary Dimensions.
Bächle, M., Schambach, M., and Heizmann, M.
2020 28th European Signal Processing Conference (EUSIPCO), 2021.
[paper] [code]
A Simulation Framework for the Design and Evaluation of Computational Cameras.
Nürnberg, T., Schambach, M., Uhlig, D., Heizmann, M., and Puente León, F.
Automated Visual Inspection and Machine Vision III (Vol. 11061, p. 1106102)
International Society for Optics and Photonics (SPIE), 2019.
[paper] [code]
Algorithms for Microlens Center Detection.
Schambach, M. and Puente León, F.
Forum Bildverarbeitung, KIT Scientific Publishing, 2018.
[paper]

A Highly Textured Real-World Multispectral Light Field Dataset.
Schambach, M. and Heizmann, M.
RADAR4KIT, 2021.
[data]
A Synthetic Multispectral Light Field Dataset for Light Field Deep Learning.
Schambach, M. and Heizmann, M.
IEEE Dataport, 2020.
[data]
Synthetic Whiteimages for the Lytro Illum Light Field Camera with Ground Truth Microlens Center Coordinates.
Schambach, M. and Puente León, F.
IEEE Dataport, 2020.
[data]

lfcnn – A TensorFlow Framework for Light Field Deep Learning.
Creator, maintainer
mdbh – A MongoDB Helper Collection to use with Sacred and Omniboard.
Co-creator, maintainer
plenpy – A Plenoptic Processing Library for Python.
Creator, maintainer
IIIT Raytracer – A Computational Camera Simulation Written in C++.
Contributor, maintainer

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