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Matthias Humt
Matthias Humt
Research Scientist (DLR), PhD Candidate (TUM)
Bestätigte E-Mail-Adresse bei tum.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
10502023
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
772023
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
592020
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
282021
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
242022
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
232022
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
23
Bayesian optimization meets laplace approximation for robotic introspection
M Humt, J Lee, R Triebel
arXiv preprint arXiv:2010.16141, 2020
142020
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
122020
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
92022
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
6
Interactive and incremental learning of spatial object relations from human demonstrations
R Kartmann, T Asfour
Frontiers in Robotics and AI 10, 1151303, 2023
4*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
42022
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
32023
Laplace approximation for uncertainty estimation of deep neural networks
M Humt
TUM, 2019
32019
Shape completion with prediction of uncertain regions
M Humt, D Winkelbauer, U Hillenbrand
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2023
22023
Unknown object grasping for assistive robotics
E Miller, M Durner, M Humt, G Quere, W Boerdijk, AM Sundaram, F Stulp, ...
2024 IEEE International Conference on Robotics and Automation (ICRA), 18157 …, 2024
12024
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
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