| Texture Fields: Learning Texture Representations in Function Space M Oechsle, L Mescheder, M Niemeyer, T Strauss, A Geiger Proceedings of the IEEE International Conference on Computer Vision, 4531-4540, 2019 | 359 | 2019 |
| CANet: An unsupervised intrusion detection system for high dimensional CAN bus data M Hanselmann, T Strauss, K Dormann, H Ulmer Ieee Access 8, 58194-58205, 2020 | 253 | 2020 |
| Ensemble methods as a defense to adversarial perturbations against deep neural networks T Strauss, M Hanselmann, A Junginger, H Ulmer arXiv preprint arXiv:1709.03423, 2017 | 190 | 2017 |
| Learning implicit surface light fields M Oechsle, M Niemeyer, C Reiser, L Mescheder, T Strauss, A Geiger 2020 International Conference on 3D Vision (3DV), 452-462, 2020 | 45 | 2020 |
| Comparison of statistical inversion with iteratively regularized Gauss Newton method for image reconstruction in electrical impedance tomography S Ahmad, T Strauss, S Kupis, T Khan Applied Mathematics and Computation 358, 436-448, 2019 | 41 | 2019 |
| Statistical inversion in electrical impedance tomography using mixed total variation and non-convex ℓp regularization prior T Strauss, T Khan Journal of Inverse and Ill-posed Problems 23 (5), 529-542, 2015 | 28 | 2015 |
| Improved Training of Physics-Informed Neural Networks Using Energy-Based Priors: a Study on Electrical Impedance Tomography A Pokkunuru, P Rooshenas, T Strauss, A Abhishek, T Khan The Eleventh International Conference on Learning Representations, 2023 | 17 | 2023 |
| High-Resolution Neural Style Transfer for Test Data Generation for ADAS/HAD Functions A Junginger, M Hanselmann, T Strauss, S Boblest, J Buchner, H Ulmer Automatisiertes Fahren 2020: Von der Fahrerassistenz zum autonomen Fahren 6 …, 2021 | 12* | 2021 |
| Statistical inversion of absolute permeability in single-phase darcy flow T Strauss, X Fan, S Sun, T Khan Procedia Computer Science 51, 1188-1197, 2015 | 10 | 2015 |
| Method and device for recognizing anomalies in a data stream of a communication network A La Marca, M Hanselmann, T Strauss US Patent App. 16/213,649, 2019 | 8 | 2019 |
| Comparison of different radial basis function networks for the electrical impedance tomography (EIT) inverse problem CA Faiyaz, P Shahrear, RA Shamim, T Strauss, T Khan Algorithms 16 (10), 461, 2023 | 6 | 2023 |
| Statistical inverse problems in electrical impedance and diffuse optical tomography T Strauss Clemson University, 2015 | 6 | 2015 |
| Implicit solutions of the electrical impedance tomography inverse problem in the continuous domain with deep neural networks T Strauss, T Khan Entropy 25 (3), 493, 2023 | 5 | 2023 |
| Syncan dataset M Hanselmann, T Strauss, K Dormann, H Ulmer Retrieved August 1, 2021, 2020 | 5 | 2020 |
| An optimal Bayesian estimator for absorption coefficient in diffuse optical tomography A Abhishek, T Strauss, T Khan SIAM Journal on Imaging Sciences 15 (2), 797-821, 2022 | 3 | 2022 |
| Method and apparatus for detecting manipulation of a technical device in a motor vehicle utilizing methods of artificial intelligence A Serout, JS Buchner, M Hanselmann, N Ide, S Nagel, T Branz, T Strauss US Patent App. 17/445,356, 2022 | 2 | 2022 |
| MCMC-Net: Accelerating Markov Chain Monte Carlo with Neural Networks for Inverse Problems S Majee, A Abhishek, T Strauss, T Khan arXiv preprint arXiv:2412.16883, 2024 | 1 | 2024 |
| Differentiable Optimization for Orchestration: Resource Offloading for Vehicles in Smart Cities T Strauss, M Oechsle, U Bauknecht IEEE Access 12, 23798-23807, 2024 | 1 | 2024 |
| Computer-implemented method and device for a manipulation detection for exhaust gas treatment systems with the aid of artificial intelligence methods M Hanselmann, JS Buchner, T Strauss, T Branz US Patent App. 17/581,416, 2022 | 1 | 2022 |
| Method and apparatus for calibrating a system for recognizing attempts to penetrate a computer network T Strauss, K Dormann, M Hanselmann US Patent 11,206,274, 2021 | 1 | 2021 |
Taufiquar KhanUniversity of North Carolina at Charlotte在 uncc.edu 的电子邮件经过验证
