This project has been sponsored by the European Union and the National Development Fund as part of the structural and contextual development of higher education in Hungary. Innsbruck: Selbstverlag des Instituts für Geographie der Universität Innsbruck, 2002.
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In his Ph D thesis, he developed a novel guided policy search algorithm for learning complex neural network control policies, which was later applied to enable a range of robotic tasks, including end-to-end training of policies for perception and control.
He has also developed algorithms for learning from demonstration, inverse reinforcement learning, efficient training of stochastic neural networks, computer vision, and data-driven character animation.
BIO: Sergey Levine is an assistant professor at the University of Washington.
His research focuses on robotics and machine learning.
Despite these impressive results, such models are a priori inappropriate models of language.
One point of criticism is that language users create and understand new words all the time, challenging the finite vocabulary assumption.
Should Model Architecture Reflect Linguistic Structure? Sequential recurrent neural networks (RNNs) over finite alphabets are remarkably effective models of natural language.
RNNs now obtain language modeling results that substantially improve over long-standing state-of-the-art baselines, as well as in various conditional language modeling tasks such as machine translation, image caption generation, and dialogue generation.