Research output per year
Research output per year
Project Details
Description
MacSeNet is Marie Sklodowska-Curie Innovative Training Network (ITN). The aim of this Innovative Training Network is to train a new generation of creative, entrepreneurial and innovative early stage researchers (ESRs) in the research area of measurement and estimation of signals using knowledge or data about the underlying structure.
With its combination of ideas from machine learning and sensing, we refer to this research topic as “Machine Sensing”. We will train all ESRs in research skills needed to obtain an internationally-recognized PhD; to experience applying their research a non-Academic sector; and to gain transferable skills such as entrepreneurship and communication skills.
We will develop new robust and efficient Machine Sensing theory and algorithms, together methods for a wide range of signals, including: advanced brain imaging; inverse imaging problems; audio and music signals; and non-traditional signals such as signals on graphs.
With its combination of ideas from machine learning and sensing, we refer to this research topic as “Machine Sensing”. We will train all ESRs in research skills needed to obtain an internationally-recognized PhD; to experience applying their research a non-Academic sector; and to gain transferable skills such as entrepreneurship and communication skills.
We will develop new robust and efficient Machine Sensing theory and algorithms, together methods for a wide range of signals, including: advanced brain imaging; inverse imaging problems; audio and music signals; and non-traditional signals such as signals on graphs.
| Acronym | MacSeNet |
|---|---|
| Status | Finished |
| Effective start/end date | 1/01/15 → 31/12/18 |
| Links | http://www.macsenet.eu/ |
Funding
- EU government bodies: £364,384.00
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: an alternative to conventional spiral MR Fingerprinting
Benjamin, A. J. V., Gómez, P. A., Golbabaee, M., Mahbub, Z., Sprenger, T., Menzel, M. I., Davies, M. & Marshall, I., 10 May 2019, (E-pub ahead of print) In: Magnetic Resonance Imaging. 21 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
The Limitation and Practical Acceleration of Stochastic Gradient Algorithms in Inverse Problems.
Tang, B. J., Egiazarian, K. & Davies, M., May 2019, 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers, 18778660 . (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; vol. 2019-May).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile -
Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes
Tang, J., Golbabaee, M., Bach, F. & Davies, M., 3 Dec 2018, Advances in Neural Information Processing Systems 31 (NeurIPS 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile