Schmitt, Michael

22 publications

CVPRW 2025 Distribution Shifts at Scale: Out-of-Distribution Detection in Earth Observation Burak Ekim, Girmaw Abebe Tadesse, Caleb Robinson, Gilles Quentin Hacheme, Michael Schmitt, Rahul Dodhia, Juan M. Lavista Ferres

CVPRW 2025 SARFormer - An Acquisition Parameter Aware Vision Transformer for Synthetic Aperture Radar Data Jonathan Prexl, Michael Recla, Michael Schmitt

CVPRW 2023 Multi-Modal Multi-Objective Contrastive Learning for Sentinel-1/2 Imagery Jonathan Prexl, Michael Schmitt

ICCVW 2023 Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-Wise Regression Anton Baumann, Thomas Roßberg, Michael Schmitt

CVPRW 2023 UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series Patrick Ebel, Vivien Sainte Fare Garnot, Michael Schmitt, Jan Dirk Wegner, Xiao Xiang Zhu

CVPRW 2022 Urban Building Classification (UBC) - A Dataset for Individual Building Detection and Classification from Satellite Imagery Xingliang Huang, Libo Ren, Chenglong Liu, Yixuan Wang, Hongfeng Yu, Michael Schmitt, Ronny Hänsch, Xian Sun, Hai Huang, Helmut Mayer

JMLR 2006 On the Complexity of Learning Lexicographic Strategies Michael Schmitt, Laura Martignon

JMLR 2005 Inner Product Spaces for Bayesian Networks Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon

NeurIPS 2005 On the Accuracy of Bounded Rationality: How Far from Optimal Is Fast and Frugal? Michael Schmitt, Laura Martignon

COLT 2004 An Improved VC Dimension Bound for Sparse Polynomials Michael Schmitt

COLT 2004 Bayesian Networks and Inner Product Spaces Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon

JMLR 2004 Some Dichotomy Theorems for Neural Learning Problems Michael Schmitt

NeCo 2002 Descartes' Rule of Signs for Radial Basis Function Neural Networks Michael Schmitt

NeCo 2002 Neural Networks with Local Receptive Fields and Superlinear VC Dimension Michael Schmitt

NeCo 2002 On the Complexity of Computing and Learning with Multiplicative Neural Networks Michael Schmitt

ALT 2002 RBF Neural Networks and Descartes' Rule of Signs Michael Schmitt

COLT 2001 Radial Basis Function Neural Networks Have Superlinear VC Dimension Michael Schmitt

NeurIPS 1999 Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks Michael Schmitt

MLJ 1999 On the Sample Complexity for Nonoverlapping Neural Networks Michael Schmitt

NeCo 1998 Identification Criteria and Lower Bounds for Perceptron-LikeLearning Rules Michael Schmitt

ALT 1998 On the Sample Complexity for Neural Trees Michael Schmitt

COLT 1997 On the Complexity of Learning for a Spiking Neuron (Extended Abstract) Wolfgang Maass, Michael Schmitt