关注
Matthias Humt
Matthias Humt
Research Scientist (DLR), PhD Candidate (TUM)
在 tum.de 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
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
8932023
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
522020
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
512023
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
272021
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
232022
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
212022
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
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
112020
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
52022
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
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
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
22022
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
12024
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
12023
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
系统目前无法执行此操作,请稍后再试。
文章 1–20