Lars Maaløe

1.3k total citations · 1 hit paper
18 papers, 389 citations indexed

About

Lars Maaløe is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Lars Maaløe has authored 18 papers receiving a total of 389 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 4 papers in Signal Processing. Recurrent topics in Lars Maaløe's work include Topic Modeling (6 papers), Music and Audio Processing (4 papers) and Machine Learning in Healthcare (4 papers). Lars Maaløe is often cited by papers focused on Topic Modeling (6 papers), Music and Audio Processing (4 papers) and Machine Learning in Healthcare (4 papers). Lars Maaløe collaborates with scholars based in Denmark, United States and Ireland. Lars Maaløe's 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 and has published in prestigious journals such as Energies, IEEE Journal of Selected Topics in Signal Processing and npj Digital Medicine.

In The Last Decade

Lars Maaløe

15 papers receiving 379 citations

Hit Papers

Self-Supervised Speech Representation Learning: A Review 2022 2026 2023 2024 2022 50 100 150
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Self-Supervised Speech Representation Learning: A Review
    2022 194 citations Abdelrahman Mohamed, Hung-yi Lee et al. IEEE Journal of Selected Topics in Signal Processing profile →

Peers

Lars Maaløe
Mouldi Bedda Algeria
A. Revathi India
Ziyan Jiang China
Masao Someki United States
C. Chandra Sekhar India
Beat Pfister Switzerland
K.K. Paliwal Australia
Vitaly Lavrukhin United States
Mouldi Bedda Algeria
Lars Maaløe
Citations per year, relative to Lars Maaløe Lars Maaløe (= 1×) peers Mouldi Bedda

Countries citing papers authored by Lars Maaløe

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

18 of 18 papers shown
1.
Maaløe, Lars, et al.. (2025). Voice as a Health Indicator: The Use of Sound Analysis and AI for Monitoring Respiratory Function. BioMedInformatics. 5(2). 31–31.
2.
3.
Maistro, Maria, et al.. (2024). An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records. Research at the University of Copenhagen (University of Copenhagen). 4869–4890. 1 indexed citations
4.
Havtorn, Jakob D., Lasse Borgholt, Stig Nikolaj Fasmer Blomberg, et al.. (2023). A retrospective study on machine learning-assisted stroke recognition for medical helpline calls. npj Digital Medicine. 6(1). 235–235. 7 indexed citations
5.
Junge, Alexander, Jakob D. Havtorn, Lasse Borgholt, et al.. (2023). Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study. arXiv (Cornell University). 2572–2582. 15 indexed citations
6.
Mohamed, Abdelrahman, Hung-yi Lee, Lasse Borgholt, et al.. (2022). Self-Supervised Speech Representation Learning: A Review. IEEE Journal of Selected Topics in Signal Processing. 16(6). 1179–1210. 194 indexed citations breakdown →
7.
Sujan, Mark, et al.. (2022). Assuring safe artificial intelligence in critical ambulance service response: study protocol. British Paramedic Journal. 7(1). 36–42. 2 indexed citations
8.
Mohamed, Abdelrahman, Hung-yi Lee, Lasse Borgholt, et al.. (2022). Self-Supervised Speech Representation Learning: A Review. arXiv (Cornell University). 2 indexed citations
9.
Maaløe, Lars, Ole Winther, Sergiu Spataru, & Dezső Séra. (2020). Condition Monitoring in Photovoltaic Systems by Semi-Supervised Machine Learning. Energies. 13(3). 584–584. 10 indexed citations
10.
Borgholt, Lasse, Jakob D. Havtorn, Żeljko Agić, et al.. (2020). Do End-to-End Speech Recognition Models Care About Context?. arXiv (Cornell University). 4352–4356. 4 indexed citations
11.
Maaløe, Lars, M. Fraccaro, Valentin Liévin, & Ole Winther. (2019). BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling. Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU). 32. 6548–6558. 28 indexed citations
12.
Liévin, Valentin, et al.. (2019). Towards Hierarchical Discrete Variational Autoencoders. 2 indexed citations
13.
Maaløe, Lars, Casper Kaae Sønderby, Søren Kaae Sønderby, & Ole Winther. (2016). Auxiliary deep generative models. International Conference on Machine Learning. 1445–1454. 76 indexed citations
14.
Sønderby, Casper Kaae, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, & Ole Winther. (2016). How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks. arXiv (Cornell University). 33 indexed citations
15.
Spataru, Sergiu, et al.. (2016). Development and implementation of a PV performance monitoring system based on inverter measurements. VBN Forskningsportal (Aalborg Universitet). 1–7. 8 indexed citations
16.
Maaløe, Lars, Casper Kaae Sønderby, Søren Kaae Sønderby, & Ole Winther. (2015). Improving Semi-Supervised Learning with Auxiliary Deep Generative Models. Neural Information Processing Systems. 6 indexed citations
17.
Maaløe, Lars, et al.. (2015). Deep Belief Nets for Topic Modeling. 1 indexed citations
18.
Maaløe, Lars, et al.. (2011). a Platform-Independent Framework for Application Development for Smart Phones.

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.

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