Institute for Advanced Simulation (IAS)
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Institute for Advanced Simulation (IAS)
The Cross-Sectional Team Deep Learning conducts basic and applied research in the field of adaptive multi-layer neural network architectures that learn complex tasks from very large amount of raw, unprocessed data.
The group's special focus is on:
Recently, major breakthroughs were achieved by deep neural networks in complex learning scenarios such as natural image understanding, speech processing, and control such as autonomous car driving or playing GO. One of the main benefits is hereby the ability of the networks to learn task relevant features directly from the raw data, in contrast to the necessity to hand craft features in previous state-of-the-art machine learning approaches.
However, most of the successful state-of-the-art deep learning architectures still rely on heavily supervised learning mode where millions of hand labeled examples are necessary for the training and the data is fed into the networks in passive, carefully well-prepared fashion without the network being able to actively request and select data of interest.
Group activities are therefore aimed towards advancing methods for active, unsupervised and reinforcement learning. In these modes, networks should be able to process the available data without any labels, exploiting data internal statistics, like for instance inherent spatial, temporal or other structural relations, or different transformations inherent in the data. These can aid to drive unsupervised learning and build predictive generative models of the observed data by inferring most probable underlying latent causes.
Moreover, in the active mode, a network should be able to execute different actions that influence what data will arrive at the network's input. Generated actions can also produce positive or negative consequences following action selection.
Using this closed loop, the network gains the ability to guide learning via prediction error signals. These signals are generated when the network is making a mistake in attempt to predict an outcome - upcoming sensory events or their valencies, good or bad - that follow previous sensory events or an executed action. Prediction error signals can then be used to incrementally correct the network's internal model and continuously improve its prediction capability.
The long term aim of this research is to establish a generic artificial intelligence type of neural network architecture that fuses supervised, unsupervised and reinforcement learning, providing following capabilities:
The group also provides support for implementation and deployment of deep neural networks for different scientific and technological applications.
The CST Deep Learning is jointly led by Dr. Jenia Jitsev and Prof. Morris Riedel.