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Inspired by previous work on emergent communication in referential games, we propose a novel multi-modal, multi-step referential game, where the sender and receiver have access to distinct modalities of an object, and their information exchange is bidirectional and of arbitrary duration.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Wordnet: a lexical database for english
George A Miller · 1995
Earlier work this paper cites.
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Hugo Larochelle, Dumitru Erhan, and Yoshua Bengio · 2008
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Steven Bird, Ewan Klein, and Edward Loper · 2009
Earlier work this paper cites.
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Earlier work this paper cites.
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Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
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Earlier work this paper cites.
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Tijmen Tieleman and Geoffrey Hinton · 2012
Earlier work this paper cites.
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Earlier work this paper cites.
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