A survey of uncertainty in deep neural networks J Gawlikowski, CRN Tassi, M Ali, J Lee, M Humt, J Feng, A Kruspe, ... Artificial Intelligence Review 56 (Suppl 1), 1513-1589, 2023 | 893 | 2023 |
Estimating model uncertainty of neural networks in sparse information form J Lee, M Humt, J Feng, R Triebel International Conference on Machine Learning, 5702-5713, 2020 | 52 | 2020 |
Blenderproc2: A procedural pipeline for photorealistic rendering M Denninger, D Winkelbauer, M Sundermeyer, W Boerdijk, MW Knauer, ... Journal of Open Source Software 8 (82), 4901, 2023 | 51 | 2023 |
A survey of uncertainty in deep neural networks. arXiv J Gawlikowski, CRN Tassi, M Ali, J Lee, M Humt, J Feng, A Kruspe, ... arXiv preprint arXiv:2107.03342, 2021 | 27 | 2021 |
A survey of uncertainty in deep neural networks. arXiv 2021 J Gawlikowski, CRN Tassi, M Ali, J Lee, M Humt, J Feng, A Kruspe, ... arXiv preprint arXiv:2107.03342, 2022 | 23 | 2022 |
Trust your robots! predictive uncertainty estimation of neural networks with sparse gaussian processes J Lee, J Feng, M Humt, MG Müller, R Triebel Conference on Robot Learning, 1168-1179, 2022 | 21 | 2022 |
A survey of uncertainty in deep neural networks. 2021 J Gawlikowski, CRN Tassi, M Ali, J Lee, M Humt, J Feng, A Kruspe, ... arXiv preprint arXiv:2107.03342, 0 | 21 | |
Bayesian optimization meets laplace approximation for robotic introspection M Humt, J Lee, R Triebel arXiv preprint arXiv:2010.16141, 2020 | 14 | 2020 |
Learning multiplicative interactions with Bayesian neural networks for visual-inertial odometry K Shinde, J Lee, M Humt, A Sezgin, R Triebel arXiv preprint arXiv:2007.07630, 2020 | 11 | 2020 |
A two-stage learning architecture that generates high-quality grasps for a multi-fingered hand D Winkelbauer, B Bäuml, M Humt, N Thuerey, R Triebel 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022 | 5 | 2022 |
others (2021). A survey of uncertainty in deep neural networks J Gawlikowski, CRN Tassi, M Ali, J Lee, M Humt, J Feng arXiv preprint arXiv:2107.03342, 0 | 5 | |
Interactive and incremental learning of spatial object relations from human demonstrations R Kartmann, T Asfour Frontiers in Robotics and AI 10, 1151303, 2023 | 3* | 2023 |
Laplace approximation for uncertainty estimation of deep neural networks M Humt TUM, 2019 | 3 | 2019 |
Shape completion with prediction of uncertain regions M Humt, D Winkelbauer, U Hillenbrand 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2023 | 2 | 2023 |
Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities J Lee, R Balachandran, K Kondak, A Coelho, M De Stefano, M Humt, ... arXiv preprint arXiv:2210.09678, 2022 | 2 | 2022 |
Unknown object grasping for assistive robotics E Miller, M Durner, M Humt, G Quere, W Boerdijk, AM Sundaram, F Stulp, ... arXiv preprint arXiv:2404.15001, 2024 | 1 | 2024 |
Combining Shape Completion and Grasp Prediction for Fast and Versatile Grasping with a Multi-Fingered Hand M Humt, D Winkelbauer, U Hillenbrand, B Bäuml 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids), 1-8, 2023 | 1 | 2023 |
DLR-IB-RM-OP-2019-108 M Humt | | |
Supplementary Materials for the Submission: Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes J Lee, J Feng, M Humt, MG Müller, R Triebel | | |
Supplementary Materials for the Submission: Estimating Model Uncertainty of Neural Networks in Sparse Information Form J Lee, M Humt, J Feng, R Triebel | | |