Следене
Jianxiang Feng
Jianxiang Feng
TUM-Technical University of Munich
Потвърден имейл адрес: tum.de - Начална страница
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Позовавания
Позовавания
Година
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
7362023
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
462020
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
J Lee, J Feng, M Humt, M Müller, R Triebel
5th Annual Conference on Robot Learning, 2021
212021
Introspective robot perception using smoothed predictions from bayesian neural networks
J Feng, M Durner, ZC Marton, F Balint-Benczedi, R Triebel
International Symposium on Robotics Research (ISRR), 06-10 Oct 2019, Hanoi …, 2019
112019
Bayesian active learning for sim-to-real robotic perception
J Feng, J Lee, M Durner, R Triebel
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022
6*2022
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
5*2022
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning
M Atad*, J Feng*, I Rodríguez, M Durner, R Triebel
arXiv preprint arXiv:2303.10135, 2023
22023
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning
J Feng, J Lee, S Geisler, S Gunnemann, R Triebel
7th Annual Conference on Robot Learning, 2023
12023
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
J Feng, M Atad, I Rodríguez, M Durner, S Günnemann, R Triebel
18th Robotics: Science and System 2023 Workshops. Robotics and AI: The …, 2023
12023
Uncertainty-Based Improvement of a Visual Classification System
J Feng
Master Thesis, Chair of Media Technology, Technische Universität München, 2019
2019
Evaluating Uncertainty-based Failure Detection for Closed-Loop LLM Planners
Z Zheng, Q Feng, A Knoll, J Feng
ICRA 2024 Workshop {\textemdash} Back to the Future: Robot Learning Going …, 0
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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