Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications

Autor: Joerg Schmitt, Eric Wong, Tim Schneider, Frank R. Schmidt, J. Zico Kolter

Publikováno v: 2020 IEEE Intelligent Vehicles Symposium (IV).

Recent work has shown that it is possible to learn neural networks with provable guarantees on the output of the model when subject to input perturbations, however these works have focused primarily on defending against adversarial examples for image

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a354d0dca5dd2d08dc35db8148203133
https://doi.org/10.1109/iv47402.2020.9304765

Comprehensive Analysis of the Alternaria Mycobolome Using Mass Spectrometry Based Metabolomics

Autor: Richard Hammerl, Stefan Asam, Basem Kanawati, Philippe Schmitt-Kopplin, Thomas Hofmann, Oliver Frank, Michael Rychlik, Marina Gotthardt, Frank R. Schmidt

Publikováno v: Mol. Nutr. Food Res. 64:1900558 (2020)

Scope Alternaria fungi are widely distributed plant pathogens infecting grains and vegetables and causing major harvest losses in the field and during postharvest storage. Besides, consumers are endangered by the formation of toxic secondary metaboli

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4391bcf9d314518e378d3f290ccc8c0e
https://pubmed.ncbi.nlm.nih.gov/31802615

Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs

Autor: Daniel Cremers, Bjoern Andres, Laura Leal-Taixé, Jonas Schupfer, Frank R. Schmidt, Csaba Domokos, Emanuel Laude, Jan-Hendrik Lange

Publikováno v: CVPR

This paper introduces a novel algorithm for transductive inference in higher-order MRFs, where the unary energies are parameterized by a variable classifier. The considered task is posed as a joint optimization problem in the continuous classifier pa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a722defc2087a94a1938b59431e405bc
https://doi.org/10.1109/cvpr.2018.00174

A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matching

Autor: Daniel Cremers, Johan Thunberg, Frank R. Schmidt, Florian Bernard

Publikováno v: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR
The proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (2017).

We propose a combinatorial solution for the problem of non-rigidly matching a 3D shape to 3D image data. To this end, we model the shape as a triangular mesh and allow each triangle of this mesh to be rigidly transformed to achieve a suitable matchin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79dbe51165737fd1d12d0a50896dc07f

Video Segmentation with Just a Few Strokes

Autor: Frank R. Schmidt, Thomas Brox, Naveen Shankar Nagaraja

Publikováno v: ICCV

As the use of videos is becoming more popular in computer vision, the need for annotated video datasets increases. Such datasets are required either as training data or simply as ground truth for benchmark datasets. A particular challenge in video se

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::76dd79179a91d90913d5cfd3202f8535
https://doi.org/10.1109/iccv.2015.370