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New Cross-Sectional Team for Deep Learning

In recent years there has been a radical transformation in the field of machine learning and artificial intelligence, catalyzed by an ensemble of methods now widely known as deep learning. These methods, which are based on adaptive neural network architectures of multiple layers, can be applied to large amounts of raw, unprocessed data to discover hidden complex patterns in the data through learning from labelled or unlabelled training examples. These examples cover a broad range of machine learning tasks such as classification, clustering, prediction, and control. In order to foster research in this area and optimize support for HPC users at JSC, a cross-sectional team (CST) has been formed at JSC dedicated to deep learning.

The team will pursue research and support activities. In addition to basic and applied research, novel architectures will be created for unsupervised and reinforcement learning, and – together with domain scientists – applications will be implemented to analyse and combine large amounts of raw data that contain valuable but hidden information which needs to be revealed. The CST Deep Learning will cooperate closely with data-intensive projects, both within Forschungszentrum Jülich and with international scientific and industrial partners. It will also provide toolsets, support, and optimized infrastructure setups with respect to deep learning methods for end users. While the group is still in the phase of being established, the team has already started to support end users in the field of remote sensing and neurosciences who were chosen through the scientific big data analytics elements in the calls for computing time of the John von Neumann Institute for Computing (NIC). The CST Deep Learning is jointly led by Dr. Jenia Jitsev and Prof. Morris Riedel.

JSC News No. 251, 18 July 2017


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