||Deep Learning for High-Tech Systems and Materials (HTSM)
||Prof. Dr. Rob van Nieuwpoort (UvA, NLeSC)
||Prof. Max Welling (UvA), Prof. Henri Bal (VU), Prof. Henk Corporaal (TU/e)
||FEI, Scyfer, Tata Steel, NLeSC, Astron, SURFsara
||DL for large scale fabrication processes and large-scale scientific instruments (steel inspection, surveillance, electron microscopes, telescopes, detectors)
||P4 addresses four main data efficient DL approaches:
- Deal efficiently with combinations of large, sparse, heterogeneous, and multimodal data.
- Combining semi-supervised DL techniques with active learning to reduce the amount of labeled data needed, and deal with extremely unbalanced clusters.
- Combine deep generative nets and ladder nets with active learning to reduce the amount of labeled data needed, and deal with extremely unbalanced clusters.
- Exploit accelerators, special-purpose hardware, and distributed services to enable real-time active learning.