Konrad Żołna
- Artificial Intelligence
- Civil and Structural Engineering
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Mechanical Engineering
- Co-authors
- Phong B. DaoTomasz BarszczWiesław J. StaszewskiPedro O. PinheiroNegar RostamzadehKrzysztof J. GerasKyunghyun ChoAlexander Novikov
- Topics
- Reinforcement Learning in Robotics (5 papers)Visual Attention and Saliency Detection (2 papers)Robot Manipulation and Learning (2 papers)
- Journals
- Mechanical Systems and Signal ProcessingComputer Vision and Image UnderstandingMathematical Problems in Engineering
- Partner nations
- PolandUnited StatesCanada
In The Last Decade
Konrad Żołna
14 papers receiving 100 citations
Peers
Comparison fields: 5 of 49
- Artificial Intelligence 41
- Civil and Structural Engineering 29
- Computer Vision and Pattern Recognition 19
- Control and Systems Engineering 15
- Mechanical Engineering 10
Countries citing papers authored by Konrad Żołna
This map shows the geographic impact of Konrad Żołna'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 Konrad Żołna with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Konrad Żołna more than expected).
Fields of papers citing papers by Konrad Żołna
This network shows the impact of papers produced by Konrad Żołna. 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 Konrad Żołna. The network helps show where Konrad Żołna may publish in the future.
Co-authorship network of co-authors of Konrad Żołna
This figure shows the co-authorship network connecting the top 25 collaborators of Konrad Żołna. A scholar is included among the top collaborators of Konrad Żołna 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 Konrad Żołna. Konrad Żołna is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | Addressing Extrapolation Error in Deep Offline Reinforcement Learning | 2 |
| 3 | RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. | 2 |
| 4 | Critic Regularized Regression | 1 |
| 5 | 2 | |
| 6 | 10 | |
| 7 | A Framework for Data-Driven Robotics | 4 |
| 8 | 25 | |
| 9 | 2 | |
| 10 | Focused Hierarchical RNNs for Conditional Sequence Processing | 9 |
| 11 | Reinforced Imitation Learning from Observations | 1 |
| 12 | 0 | |
| 13 | 3 | |
| 14 | 21 | |
| 15 | 16 |
About Konrad Żołna
Konrad Żołna is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Science Applications, having authored 15 papers that have together received 101 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (5 papers), Visual Attention and Saliency Detection (2 papers) and Robot Manipulation and Learning (2 papers). The work is most often cited by research in Computational Mathematics (1 citation), Civil and Structural Engineering (29 citations) and Artificial Intelligence (41 citations). Konrad Żołna has collaborated with scholars based in Poland, United States and Canada. Frequent co-authors include Phong B. Dao, Tomasz Barszcz, Wiesław J. Staszewski, Pedro O. Pinheiro, Negar Rostamzadeh, Krzysztof J. Geras, Kyunghyun Cho, Alexander Novikov, Sergio Gómez Colmenarejo and Joëlle Pineau. Their work appears in journals such as Mechanical Systems and Signal Processing, Computer Vision and Image Understanding and Mathematical Problems in Engineering.
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.