Jakub Konečný
- Artificial Intelligence top 5%
- Computational Mechanics top 10%
- Computer Networks and Communications top 10%
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition
- Co-authors
- Peter RichtárikMartin TakáčJie LiuZheng QuChenxin MaVirginia SmithMichael I. JordanMartin Jaggi
- Topics
- Stochastic Gradient Optimization Techniques (7 papers)Sparse and Compressive Sensing Techniques (6 papers)Complexity and Algorithms in Graphs (2 papers)
- Journals
- IEEE Journal of Selected Topics in Signal ProcessingOptimization methods & softwareFrontiers in Applied Mathematics and Statistics
- Partner nations
- United KingdomUnited StatesSlovakia
In The Last Decade
Jakub Konečný
9 papers receiving 382 citations
Peers
Comparison fields: 5 of 64
- Artificial Intelligence 293
- Computational Mechanics 110
- Computer Networks and Communications 109
- Electrical and Electronic Engineering 78
- Computer Vision and Pattern Recognition 42
Countries citing papers authored by Jakub Konečný
This map shows the geographic impact of Jakub Konečný'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 Jakub Konečný with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jakub Konečný more than expected).
Fields of papers citing papers by Jakub Konečný
This network shows the impact of papers produced by Jakub Konečný. 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 Jakub Konečný. The network helps show where Jakub Konečný may publish in the future.
Co-authorship network of co-authors of Jakub Konečný
This figure shows the co-authorship network connecting the top 25 collaborators of Jakub Konečný. A scholar is included among the top collaborators of Jakub Konečný 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 Jakub Konečný. Jakub Konečný is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 34 | |
| 2 | 1 | |
| 3 | 55 | |
| 4 | 123 | |
| 5 | Stop wasting my gradients: practical SVRG | 15 |
| 6 | 149 | |
| 7 | Simple Complexity Analysis of Direct Search. | 4 |
| 8 | S2CD: Semi-stochastic coordinate descent | 7 |
| 9 | 1 |
About Jakub Konečný
Jakub Konečný is a scholar working on Computational Mechanics, Artificial Intelligence and Numerical Analysis, having authored 9 papers that have together received 389 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (7 papers), Sparse and Compressive Sensing Techniques (6 papers) and Complexity and Algorithms in Graphs (2 papers). The work is most often cited by research in Artificial Intelligence (293 citations), Computer Science Applications (33 citations) and Computational Mechanics (110 citations). Jakub Konečný has collaborated with scholars based in United Kingdom, United States and Slovakia. Frequent co-authors include Peter Richtárik, Martin Takáč, Jie Liu, Zheng Qu, Chenxin Ma, Virginia Smith, Michael I. Jordan, Martin Jaggi, Mark Schmidt and Petr Dzurenda. Their work appears in journals such as IEEE Journal of Selected Topics in Signal Processing, Optimization methods & software and Frontiers in Applied Mathematics and Statistics.
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.