Matthias Humt
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Control and Systems Engineering top 10%
- Aerospace Engineering
- Electrical and Electronic Engineering
- Co-authors
- Rudolph TriebelJong‐Seok LeeJianxiang FengMohsin AliWen YangRichard BamlerAnna KruspeMuhammad Shahzad
- Topics
- Robot Manipulation and Learning (4 papers)Advanced Vision and Imaging (3 papers)Adversarial Robustness in Machine Learning (3 papers)
- Journals
- Artificial Intelligence ReviewUniversity of Twente Research InformationInternational Conference on Machine Learning
- Partner nations
- GermanyUnited StatesUnited Kingdom
In The Last Decade
Matthias Humt
11 papers receiving 580 citations
Hit Papers
Peers
Comparison fields: 5 of 115
- Artificial Intelligence 213
- Computer Vision and Pattern Recognition 121
- Control and Systems Engineering 99
- Aerospace Engineering 46
- Electrical and Electronic Engineering 44
Countries citing papers authored by Matthias Humt
This map shows the geographic impact of Matthias Humt's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matthias Humt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthias Humt more than expected).
Fields of papers citing papers by Matthias Humt
This network shows the impact of papers produced by Matthias Humt. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matthias Humt. The network helps show where Matthias Humt may publish in the future.
Co-authorship network of co-authors of Matthias Humt
This figure shows the co-authorship network connecting the top 25 collaborators of Matthias Humt. A scholar is included among the top collaborators of Matthias Humt based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Matthias Humt. Matthias Humt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 6 | |
| 4 | 55 | |
| 5 | A survey of uncertainty in deep neural networksbreakdown → | 509 |
| 6 | 2 | |
| 7 | 2 | |
| 8 | 8 | |
| 9 | 2 | |
| 10 | Estimating Model Uncertainty of Neural Network in Sparse Information Form | 3 |
| 11 | Laplace Approximation for Uncertainty Estimation of Deep Neural Networks | 2 |
About Matthias Humt
Matthias Humt is a scholar working on Human-Computer Interaction, Computer Graphics and Computer-Aided Design and Control and Systems Engineering, having authored 11 papers that have together received 591 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (4 papers), Advanced Vision and Imaging (3 papers) and Adversarial Robustness in Machine Learning (3 papers). The work is most often cited by research in Health Informatics (10 citations), Artificial Intelligence (213 citations) and Computer Vision and Pattern Recognition (121 citations). Matthias Humt has collaborated with scholars based in Germany, United States and United Kingdom. Frequent co-authors include Rudolph Triebel, Jong‐Seok Lee, Jianxiang Feng, Mohsin Ali, Wen Yang, Richard Bamler, Anna Kruspe, Muhammad Shahzad, Xiao Xiang Zhu and Jakob Gawlikowski. Their work appears in journals such as Artificial Intelligence Review, University of Twente Research Information and International Conference on Machine Learning.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.