Roman Novak
About
In The Last Decade
Roman Novak
39 papers receiving 304 citations
Peers
Comparison fields: 5 of 80
- Artificial Intelligence 144
- Electrical and Electronic Engineering 77
- Computer Networks and Communications 52
- Computer Vision and Pattern Recognition 49
- Radiation 46
Countries citing papers authored by Roman Novak
This map shows the geographic impact of Roman Novak'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 Roman Novak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roman Novak more than expected).
Fields of papers citing papers by Roman Novak
This network shows the impact of papers produced by Roman Novak. 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 Roman Novak. The network helps show where Roman Novak may publish in the future.
Co-authorship network of co-authors of Roman Novak
This figure shows the co-authorship network connecting the top 25 collaborators of Roman Novak. A scholar is included among the top collaborators of Roman Novak 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 Roman Novak. Roman Novak is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | Finite Versus Infinite Neural Networks: an Empirical Study | 5 |
| 4 | Neural Tangents: Fast and Easy Infinite Neural Networks in Python | 9 |
| 5 | 6 | |
| 6 | 3 | |
| 7 | Channel impulse response based vehicle analysis in tunnels | 3 |
| 8 | Sensitivity and Generalization in Neural Networks: an Empirical Study | 18 |
| 9 | Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes. | 5 |
| 10 | Deep Neural Networks as Gaussian Processes | 76 |
| 11 | 4 | |
| 12 | 1 | |
| 13 | 4 | |
| 14 | Viability of ISI-based TETRA over satellite | 4 |
| 15 | 1 | |
| 16 | 4 | |
| 17 | Comparison of WiMAX field measurements and empirical path loss model in urban and suburban environment | 4 |
| 18 | 12 | |
| 19 | Steiner Tree Balancing in Distributed Algorithm for Multicast Connection Setup. | 1 |
| 20 | 1 |
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.