Asier Mujika
Impact in
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- Domain Adaptation and Few-Shot Learning
- Topic Modeling
- Speech Recognition and Synthesis
- Evolutionary Algorithms and Applications
- Metaheuristic Optimization Algorithms Research
- Natural Language Processing Techniques
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- Music and Audio Processing
Papers in
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- Evolutionary Algorithms and Applications 2
- Metaheuristic Optimization Algorithms Research 2
- Machine Learning and Data Classification 2
- Topic Modeling 1
- Speech Recognition and Synthesis 1
- Machine Learning in Healthcare 1
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- Advanced Multi-Objective Optimization Algorithms 2
- Co-authors
- Florian Meier (4 shared papers)Angelika Steger (5 shared papers)Anders Martinsson (1 shared paper)Johannes Lengler (2 shared papers)
- Journals
- Theoretical Computer Science (1 paper)Neural Information Processing Systems (1 paper)Proceedings of the Genetic and Evolutionary Computation Conference (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- Switzerland
In The Last Decade
Asier Mujika
5 papers receiving 31 citations
Peers
Comparison fields: 5 of 29
- Artificial Intelligence 24
- Signal Processing 4
- Computer Vision and Pattern Recognition 6
- Computational Theory and Mathematics 4
- Statistical and Nonlinear Physics 2
Countries citing papers authored by Asier Mujika
This map shows the geographic impact of Asier Mujika'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 Asier Mujika with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Asier Mujika more than expected).
Fields of papers citing papers by Asier Mujika
This network shows the impact of papers produced by Asier Mujika. 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 Asier Mujika. The network helps show where Asier Mujika may publish in the future.
Co-authors
The 4 scholars most cited alongside Asier Mujika, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Fast-Slow Recurrent Neural Networks | 2017 | 17 |
| 2 | 2019 | 8 | |
| 3 | Approximating Real-Time Recurrent Learning with Random Kronecker Factors | 2018 | 5 |
| 4 | 2019 | 3 | |
| 5 | 2018 | 1 | |
| 6 | 2022 | 0 |
About Asier Mujika
Asier Mujika is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Signal Processing and Infectious Diseases, having authored 6 papers that have together received 34 indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (2 papers), Advanced Multi-Objective Optimization Algorithms (2 papers), Metaheuristic Optimization Algorithms Research (2 papers), Machine Learning and Data Classification (2 papers), Topic Modeling (1 paper), Speech Recognition and Synthesis (1 paper), Machine Learning in Healthcare (1 paper) and Time Series Analysis and Forecasting (1 paper). The work is most often cited by research in Artificial Intelligence (24 citations), Signal Processing (4 citations), Computer Vision and Pattern Recognition (6 citations), Computational Theory and Mathematics (4 citations) and Statistical and Nonlinear Physics (2 citations). Asier Mujika has collaborated with scholars based in Switzerland. Frequent co-authors include Florian Meier, Angelika Steger, Anders Martinsson and Johannes Lengler. Their work appears in journals such as Theoretical Computer Science, Neural Information Processing Systems, Proceedings of the Genetic and Evolutionary Computation Conference and arXiv (Cornell University).
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.