Projects per year
Organisation profile
Organisation profile
The Machine Learning group conducts research in advancing machine learning methods and developing applications for a variety of signals and data, aiming to provide advanced AI solutions to real-world problems. The group focuses its research on learning theory and algorithms, deep learning, representation learning, learning from speech and multimodal data, and applications of machine learning.
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The fingerprint consists of concepts related to the research profile of the unit and associated persons. It is updated automatically, when new content is added.
Collaborations from the last five years
Dive into details by clicking on the dots.
Profiles
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Peyman Aghabeyki
- The Technical Faculty of IT and Design - PhD Fellow, Research assistant
- Department of Electronic Systems - PhD Fellow, Research assistant
- Antennas, Propagation and Millimetre-Wave Systems - PhD Fellow, Research assistant
- Antennas
- Machine Learning
Person: VIP
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Mohamad Al Ahdab
- The Technical Faculty of IT and Design - Post.Doc., Research assistant
- Department of Electronic Systems - Post.Doc., Research assistant
- Automation & Control - Post.Doc., Research assistant
- Learning and Decisions Lab
- Machine Learning
- Nonlinear and Optimal Control Lab
Person: VIP
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Mohammad Bokaei
- The Technical Faculty of IT and Design - PhD Fellow
- Department of Electronic Systems - PhD Fellow
- Artificial Intelligence and Sound - PhD Fellow
- AI for Communication
- Machine Learning
Person: VIP
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Self-supervised learning for decoding audio signals
Yadav, S., Tan, Z., Theodoridis, S. & Hansen, L. K.
01/10/2022 → 30/09/2025
Project: PhD Project
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Research output
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Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review
Prelaj, A., Miskovic, V., Zanitti, M., Trovo, F., Genova, C., Viscardi, G., Rebuzzi, S. E., Mazzeo, L., Provenzano, L., Kosta, S., Favali, M., Spagnoletti, A., Castelo-Branco, L., Dolezal, J., Pearson, A. T., Lo Russo, G., Proto, C., Ganzinelli, M., Giani, C., Ambrosini, E., & 10 others, Jan 2024, In: Annals of Oncology. 35, 1, p. 29-65 37 p.Research output: Contribution to journal › Review article › peer-review
4 Citations (Scopus) -
Comments on "Feedback Capacity of Stationary Gaussian Channels"
Derpich, M. & Østergaard, J., 1 Mar 2024, In: I E E E Transactions on Information Theory. 70, 3, p. 1848-1851 4 p.Research output: Contribution to journal › Journal article › Research › peer-review
2 Citations (Scopus) -
Cortical linear encoding and decoding of sounds: Similarities and differences between naturalistic speech and music listening
Simon, A. M. D., Bech, S., Loquet, G. & Østergaard, J., 15 Jan 2024, (Accepted/In press) In: European Journal of Neuroscience.Research output: Contribution to journal › Journal article › Research
Open Access
Datasets
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Can real-ear insertion gain deviations from generic fitting prescriptions predict self-reported outcomes?
Narayanan, S. K. (Creator), Rye, P. (Creator), Piechowiak, T. (Creator), Ravn, G. (Creator), Wolff, A. (Creator), Houmøller, S. S. (Creator), Schmidt, J. H. (Creator) & Hammershøi, D. (Creator), Taylor & Francis, 7 Apr 2022
DOI: 10.6084/m9.figshare.19538156.v1, https://tandf.figshare.com/articles/journal_contribution/Can_real-ear_insertion_gain_deviations_from_generic_fitting_prescriptions_predict_self-reported_outcomes_/19538156/1
Dataset: Supplementary material
Activities
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António Pedro Aguiar
Rafal Wisniewski (Host)
31 Oct 2023 → 3 Nov 2023Activity: Hosting a guest lecturer
Press/Media
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Innovationsfondens fagudvalg får nye medlemmer
Claus Leth Bak & Jan Østergaard
18/12/2023
1 item of Media coverage
Press/Media: Press / Media
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Samarbejder på tværs skal klæde os på til fremtidens smarte vandsystemer
Jonas Falzarano Jessen, Astrid Oberborbeck Andersen, Rafal Wisniewski, Saruch Satishkumar Rathore & Carsten Kallesøe
25/04/2023
2 Media contributions
Press/Media: Press / Media
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Algoritmer skal beskytte blandt andet vandnetværk mod hackerangreb
13/09/2022
1 item of Media coverage
Press/Media: Press / Media