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Deep Learning

Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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Backpropagation and the brain

It is argued that the key principles underlying backprop may indeed have a role in brain function and induce neural activities whose differences can be used to locally approximate these signals and hence drive effective learning in deep networks in the brain.
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Deep Learning in Natural Language Processing

This chapter provides an introduction to the basics of natural language processing (NLP) as an integral part of artificial intelligence, and surveys the historical development of NLP, spanning over five decades, in terms of three waves.
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Shape Rfprfsfntatton in Parallel Systems

APSTRACT There has been a recent revival of interest in parallel systems in which computation is performed by excitatory and inhibitory interactions within a network of relatively simple, neuronlike…

P IX 2 SEQ : A L ANGUAGE M ODELING F RAMEWORK FOR O BJECT D ETECTION

Pix2Seq is presented, a simple and generic framework for object detection that achieves competitive results on the challenging COCO dataset, compared to highly specialized and well optimized detection algorithms.

Guest Editorial: Deep Learning

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