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Human infants learn the names of objects and develop their own conceptual systems without explicit supervision.
Distributional structure
Zellig S Harris · 1954
Earlier work this paper cites.
The hungarian method for the assignment problem
Harold W Kuhn · 1955
Earlier work this paper cites.
Infants’ detection of the sound patterns of words in fluent speech
Peter W Jusczyk and Richard N Aslin · 1995
Earlier work this paper cites.
Perceptual categorization of cat and dog silhouettes by 3-to 4-month-old infants
Paul C Quinn, Peter D Eimas, and Michael J Tarr · 2001
Earlier work this paper cites.
Nltk: the natural language toolkit
Steven Bird · 2006
Earlier work this paper cites.
On the origins of the conceptual system
Jean M Mandler · 2007
Earlier work this paper cites.
Rapid word learning under uncertainty via cross-situational statistics
Chen Yu and Linda B Smith · 2007
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Contributions of infant word learning to language development
Daniel Swingley · 2009
Earlier work this paper cites.
A probabilistic computational model of cross-situational word learning
Afsaneh Fazly, Afra Alishahi, and Suzanne Stevenson · 2010
Earlier work this paper cites.
At 6–9 months, human infants know the meanings of many common nouns
Elika Bergelson and Daniel Swingley · 2012
Earlier work this paper cites.
Word and object
Willard Van Orman Quine · 2013
Earlier work this paper cites.
Memory constraints on infants’ cross-situational statistical learning
Haley A Vlach and Scott P Johnson · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Cross-situational statistical word learning in young children
Sumarga H Suanda, Nassali Mugwanya, and Laura L Namy · 2014
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Learning word meanings from images of natural scenes
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The everyday statistics of objects and their names: How word learning gets its start
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Oscar: Object-semantics aligned pre-training for vision-language tasks
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Learning as the unsupervised alignment of conceptual systems
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