Lars Maaløe
- Artificial Intelligence top 5%
- Signal Processing top 5%
- Computer Vision and Pattern Recognition top 10%
- Experimental and Cognitive Psychology
- Cognitive Neuroscience
- Co-authors
- Ole WintherSøren Kaae SønderbyCasper Kaae SønderbyJakob D. HavtornLasse BorgholtChristian IgelHung-yi LeeShinji Watanabe
- Topics
- Topic Modeling (6 papers)Music and Audio Processing (4 papers)Machine Learning in Healthcare (4 papers)
- Partner nations
- DenmarkUnited StatesIreland
In The Last Decade
Lars Maaløe
15 papers receiving 379 citations
Hit Papers
Peers
Comparison fields: 5 of 72
- Artificial Intelligence 281
- Signal Processing 121
- Computer Vision and Pattern Recognition 103
- Experimental and Cognitive Psychology 22
- Cognitive Neuroscience 16
Countries citing papers authored by Lars Maaløe
This map shows the geographic impact of Lars Maaløe'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 Lars Maaløe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lars Maaløe more than expected).
Fields of papers citing papers by Lars Maaløe
This network shows the impact of papers produced by Lars Maaløe. 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 Lars Maaløe. The network helps show where Lars Maaløe may publish in the future.
Co-authorship network of co-authors of Lars Maaløe
This figure shows the co-authorship network connecting the top 25 collaborators of Lars Maaløe. A scholar is included among the top collaborators of Lars Maaløe 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 Lars Maaløe. Lars Maaløe 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 | 0 | |
| 3 | 1 | |
| 4 | 7 | |
| 5 | 15 | |
| 6 | Self-Supervised Speech Representation Learning: A Reviewbreakdown → | 194 |
| 7 | 2 | |
| 8 | 2 | |
| 9 | 10 | |
| 10 | 4 | |
| 11 | BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling | 28 |
| 12 | Towards Hierarchical Discrete Variational Autoencoders | 2 |
| 13 | Auxiliary deep generative models | 76 |
| 14 | How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks | 33 |
| 15 | 8 | |
| 16 | Improving Semi-Supervised Learning with Auxiliary Deep Generative Models | 6 |
| 17 | Deep Belief Nets for Topic Modeling | 1 |
| 18 | a Platform-Independent Framework for Application Development for Smart Phones | 0 |
About Lars Maaløe
Lars Maaløe is a scholar working on Issues, ethics and legal aspects, Artificial Intelligence and Signal Processing, having authored 18 papers that have together received 389 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Music and Audio Processing (4 papers) and Machine Learning in Healthcare (4 papers). The work is most often cited by research in Signal Processing (121 citations), Artificial Intelligence (281 citations) and Computer Vision and Pattern Recognition (103 citations). Lars Maaløe has collaborated with scholars based in Denmark, United States and Ireland. Frequent co-authors include Ole Winther, Søren Kaae Sønderby, Casper Kaae Sønderby, Jakob D. Havtorn, Lasse Borgholt, Christian Igel, Hung-yi Lee, Shinji Watanabe, Shang-Wen Li and Karen Livescu. Their work appears in journals such as Energies, IEEE Journal of Selected Topics in Signal Processing and npj Digital Medicine.
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.