Stefan Falkner
- Artificial Intelligence top 5%
- Computational Theory and Mathematics top 5%
- Computer Vision and Pattern Recognition top 10%
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering
- Co-authors
- Frank HutterAaron KleinJost Tobias SpringenbergStefan BoettcherRenato PortugalSimon BartelsPhilipp HennigMargret Keuper
- Topics
- Quantum Computing Algorithms and Architecture (5 papers)Machine Learning and Algorithms (3 papers)Quantum-Dot Cellular Automata (3 papers)
- Cited by
- Artificial IntelligenceComputational Theory and MathematicsComputer Vision and Pattern Recognition
- Journals
- Physical Review AElectronic Journal of StatisticsJournal of Physics Conference Series
- Partner nations
- GermanyUnited StatesBrazil
In The Last Decade
Stefan Falkner
10 papers receiving 309 citations
Peers
Comparison fields: 5 of 64
- Artificial Intelligence 242
- Computational Theory and Mathematics 79
- Computer Vision and Pattern Recognition 62
- Atomic and Molecular Physics, and Optics 57
- Electrical and Electronic Engineering 31
Countries citing papers authored by Stefan Falkner
This map shows the geographic impact of Stefan Falkner'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 Stefan Falkner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefan Falkner more than expected).
Fields of papers citing papers by Stefan Falkner
This network shows the impact of papers produced by Stefan Falkner. 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 Stefan Falkner. The network helps show where Stefan Falkner may publish in the future.
Co-authorship network of co-authors of Stefan Falkner
This figure shows the co-authorship network connecting the top 25 collaborators of Stefan Falkner. A scholar is included among the top collaborators of Stefan Falkner 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 Stefan Falkner. Stefan Falkner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders | 1 |
| 2 | Practical Hyperparameter Optimization for Deep Learning | 12 |
| 3 | Learning Curve Prediction with Bayesian Neural Networks | 67 |
| 4 | 43 | |
| 5 | Bayesian optimization with robust Bayesian neural networks | 119 |
| 6 | 19 | |
| 7 | 14 | |
| 8 | 9 | |
| 9 | 32 | |
| 10 | 8 |
About Stefan Falkner
Stefan Falkner is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Signal Processing, having authored 10 papers that have together received 324 indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (5 papers), Machine Learning and Algorithms (3 papers) and Quantum-Dot Cellular Automata (3 papers). The work is most often cited by research in Artificial Intelligence (242 citations), Computational Theory and Mathematics (79 citations) and Computer Vision and Pattern Recognition (62 citations). Stefan Falkner has collaborated with scholars based in Germany, United States and Brazil. Frequent co-authors include Frank Hutter, Aaron Klein, Jost Tobias Springenberg, Stefan Boettcher, Renato Portugal, Simon Bartels, Philipp Hennig and Margret Keuper. Their work appears in journals such as Physical Review A, Electronic Journal of Statistics and Journal of Physics Conference Series.
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.