Next-Generation data-efficient deep learning
(The FCAI research programs are currently in a ramp-up phase. More information will be updated here later.)
FCAI research program Next-generation data-efficient deep learning contributes to research objective Data efficiency (objective I) by developing methods which harness the power of deep learning while achieving good results with less training data and in particular less human supervision. These methods include semi-supervised learning, few-shot learning for making use of auxiliary sources of training data, and learning models that can be reliably used in simulator-based inference.
The research in Next-generation data-efficient deep learning aims to create one of the first AI systems capable of limited analogue reasoning. New insights will be brought by using simulation techniques to deep learning techniques, such as generative adversarial learning (GAN) and variational autoencoders (VAE) through their connections to Approximate Bayesian Computation (ABC). This will enable applying deep neural networks in industrial process optimization, enhanced perception in autonomous driving, or novel human-computer interaction.
The groups of following professors already take part in the research program Next-generation data-efficient deep learning. The list is currently under construction. If your group is already involved and needs listing here, please contact the program coordinator.
Alexander Ilin, Aalto University – responsible coordinator
Juho Kannala, Aalto University
Samuel Kaski, Aalto University
Arto Klami, University of Helsinki
Mikko Kurimo, Aalto University
Leo Kärkkäinen, Nokia Bell Labs, Aalto University
Jorma Laaksonen, Aalto University
Jaakko Lehtinen, Aalto University, NVIDIA
Jörg Tiedemann, University of Helsinki
Harri Valpola, Curious AI Inc. – responsible coordinator
Aki Vehtari, Aalto University