Mathias Berglund
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Signal Processing
- Electrical and Electronic Engineering
- Computer Networks and Communications
- Co-authors
- Tapani RaikoKlaus GreffJelena LuketinaMikko HonkalaLeo KärkkäinenJuha KarhunenKyunghyun ChoGuillaume Alain
- Topics
- Neural Networks and Applications (2 papers)Music and Audio Processing (2 papers)Generative Adversarial Networks and Image Synthesis (2 papers)
- Journals
- Neural NetworksarXiv (Cornell University)Neural Information Processing Systems
- Partner nations
- FinlandSwitzerlandCanada
In The Last Decade
Mathias Berglund
5 papers receiving 238 citations
Peers
Comparison fields: 5 of 78
- Artificial Intelligence 137
- Computer Vision and Pattern Recognition 80
- Signal Processing 32
- Electrical and Electronic Engineering 17
- Computer Networks and Communications 16
Countries citing papers authored by Mathias Berglund
This map shows the geographic impact of Mathias Berglund'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 Mathias Berglund with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathias Berglund more than expected).
Fields of papers citing papers by Mathias Berglund
This network shows the impact of papers produced by Mathias Berglund. 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 Mathias Berglund. The network helps show where Mathias Berglund may publish in the future.
Co-authorship network of co-authors of Mathias Berglund
This figure shows the co-authorship network connecting the top 25 collaborators of Mathias Berglund. A scholar is included among the top collaborators of Mathias Berglund 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 Mathias Berglund. Mathias Berglund is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Scalable gradient-based tuning of continuous regularization hyperparameters | 22 |
| 2 | 33rd International Conference on Machine Learning, ICML 2016 | 113 |
| 3 | Bidirectional recurrent neural networks as generative models | 65 |
| 4 | Techniques for Learning Binary Stochastic Feedforward Neural Networks | 23 |
| 5 | 24 |
About Mathias Berglund
Mathias Berglund is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence, having authored 5 papers that have together received 247 indexed citations. Recurring topics across this work include Neural Networks and Applications (2 papers), Music and Audio Processing (2 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). The work is most often cited by research in Computational Mathematics (3 citations), Artificial Intelligence (137 citations) and Computer Vision and Pattern Recognition (80 citations). Mathias Berglund has collaborated with scholars based in Finland, Switzerland and Canada. Frequent co-authors include Tapani Raiko, Klaus Greff, Jelena Luketina, Mikko Honkala, Leo Kärkkäinen, Juha Karhunen, Kyunghyun Cho, Guillaume Alain and Laurent Dinh. Their work appears in journals such as Neural Networks, arXiv (Cornell University) and Neural Information Processing Systems.
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.