Martin Ferianc
- Computer Vision and Pattern Recognition top 10%
- Electrical and Electronic Engineering
- Artificial Intelligence
- Pulmonary and Respiratory Medicine
- Hardware and Architecture
- Co-authors
- Hongxiang FanWayne LukXinyu NiuShuanglong LiuZhiqiang QueMiguel R. D. RodriguesСhen LiuHo-Cheung Ng
- Topics
- Advanced Neural Network Applications (11 papers)Adversarial Robustness in Machine Learning (7 papers)CCD and CMOS Imaging Sensors (5 papers)
- Partner nations
- United KingdomChinaSlovakia
In The Last Decade
Martin Ferianc
20 papers receiving 268 citations
Peers
Comparison fields: 5 of 68
- Computer Vision and Pattern Recognition 118
- Electrical and Electronic Engineering 98
- Artificial Intelligence 89
- Pulmonary and Respiratory Medicine 32
- Hardware and Architecture 24
Countries citing papers authored by Martin Ferianc
This map shows the geographic impact of Martin Ferianc'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 Martin Ferianc with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martin Ferianc more than expected).
Fields of papers citing papers by Martin Ferianc
This network shows the impact of papers produced by Martin Ferianc. 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 Martin Ferianc. The network helps show where Martin Ferianc may publish in the future.
Co-authorship network of co-authors of Martin Ferianc
This figure shows the co-authorship network connecting the top 25 collaborators of Martin Ferianc. A scholar is included among the top collaborators of Martin Ferianc 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 Martin Ferianc. Martin Ferianc is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 3 | |
| 5 | 14 | |
| 6 | 2 | |
| 7 | 4 | |
| 8 | 26 | |
| 9 | 10 | |
| 10 | 0 | |
| 11 | Optimizing Bayesian Recurrent Neural Networks on an FPGA-based
\n Accelerator | 5 |
| 12 | 4 | |
| 13 | 17 | |
| 14 | 7 | |
| 15 | 17 | |
| 16 | 19 | |
| 17 | 6 | |
| 18 | 39 | |
| 19 | 8 | |
| 20 | 57 |
About Martin Ferianc
Martin Ferianc is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering and Artificial Intelligence, having authored 22 papers that have together received 273 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (11 papers), Adversarial Robustness in Machine Learning (7 papers) and CCD and CMOS Imaging Sensors (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (118 citations), Hardware and Architecture (24 citations) and Artificial Intelligence (89 citations). Martin Ferianc has collaborated with scholars based in United Kingdom, China and Slovakia. Frequent co-authors include Hongxiang Fan, Wayne Luk, Xinyu Niu, Shuanglong Liu, Zhiqiang Que, Miguel R. D. Rodrigues, Сhen Liu, Ho-Cheung Ng, Chuling Fang and Hui Huang. Their work appears in journals such as PLoS ONE, Scientific Reports and International Journal of Pharmaceutics.
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.