A computer that recognises dangerous situations on security footage: this is possible with deep-learning automated systems. But before this kind of system can operate independently, you have to design it and then train it with a huge number of examples. In addition, you need considerable computing power to let the system make decisions. At the Efficient Deep Learning programme, researchers are going to make deep learning much more efficient by using examples from daily life. They want to make it possible to use the technique (Of: They want to make the technique applicable) for other automatic visual inspections, tissue analysis, smart maintenance of equipment and intelligent hearing aids that can handle noisy environments.
Programme manager: Professor H. Corporaal (Eindhoven University of Technology)
Participants: AIIR Innovations, ASTRON, CWI, Cyclomedia, Cygnify, Donders Institute, FEI, 2getthere, GN Hearing, Holst Centre, ING, Intel, Irdeto, Lely, Mobiquity, NLeSC, NXP, NVIDIA, Océ, Radboudumc, Schiphol, Scyfer, Sectra, Semiotic Labs, Siemens, Sightcorp, Sorama, SURFsara, TASS International, Tata Steel, TU Dresden, Delft University of Technology, Eindhoven University of Technology, Thales, TNO, TomTom, University of Twente, University of Amsterdam, 3DUniversum, VicarVision, ViNotion, VU Amsterdam, Wageningen University & Research
[ NWO subsidy for efficient deep learning systems (VU) ]
[ Perspectiefprogramma ‘deep learning’ geleid door TU/e ]
[ 32 million euro for top-level technological research (NWO) ]