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Strong inductive biases allow children to learn in fast and adaptable ways.
Children’s use of mutual exclusivity to constrain the meanings of words
Ellen M. Markman and Gwyn F. Wachtel · 1988
Earlier work this paper cites.
The importance of shape in early lexical learning
Barbara Landau, Linda B. Smith, and Susan S. Jones · 1988
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Categorization and Naming in Children
Ellen M Markman · 1989
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Rethinking Eliminative Connectionism
Gary F. Marcus · 1998
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How Children Learn the Meanings of Words
P Bloom · 2000
Earlier work this paper cites.
Object name learning provides on-the-job training for attention
Linda B Smith, Susan S Jones, Barbara Landau, Lisa Gershkoff-Stowe, and Larissa Samuelson · 2002
Earlier work this paper cites.
The Algebraic Mind: Integrating Connectionism and Cognitive Science
Gary F Marcus · 2003
Earlier work this paper cites.
The parallel distributed processing approach to semantic cognition
J L McClelland and T T Rogers · 2003
Earlier work this paper cites.
From the lexicon to expectations about kinds: a role for associative learning
Eliana Colunga and Linda B Smith · 2005
Earlier work this paper cites.
Guidelines for word alignment evaluation and manual alignment
Patrik Lambert, Adrià De Gispert, Rafael Banchs, and José B Mariño · 2005
Earlier work this paper cites.
Infants rapidly learn word-referent mappings via cross-situational statistics
Linda Smith and Chen Yu · 2008
Earlier work this paper cites.
Using speakers’ referential intentions to model early cross-situational word learning: Research article
Michael C Frank, Noah D Goodman, and Joshua B Tenenbaum · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
Earlier work this paper cites.
An associative model of adaptive inference for learning word-referent mappings
George Kachergis, Chen Yu, and Richard M. Shiffrin · 2012
Earlier work this paper cites.
Word learning emerges from the interaction of online referent selection and slow associative learning
Bob McMurray, Jessica S Horst, and Larissa K Samuelson · 2012
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Cited alongside, same era.
Combined spoken language translation
Markus Freitag, Joern Wuebker, Stephan Peitz, Hermann Ney, Matthias Huck, Alexandra Birch, Nadir Durrani, Philipp Koehn, Mohammed Mediani, Isabel Slawik, et al · 2014
Cited alongside, same era.
On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Learning word meanings from images of natural scenes
Akos Kadar, Afra Alishahi, and Grzegorz Chrupala · 2015
A developmental approach to machine learning?
Linda B Smith and Lauren K Slone · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Learning inductive biases with simple neural networks
Reuben Feinman and Brenden M Lake · 2018
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Deep convolutional networks do not classify based on global object shape
Nicholas Baker Id, Hongjing Lu, Gennady Erlikhman Id, and Philip J Kellman · 2018
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Bayesian word learning in multiple language environments
Benjamin D Zinszer, Sebi V Rolotti, Fan Li, and Ping Li · 2018
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Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks
Brenden M Lake and Marco Baroni · 2018
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Adam: A method for stochastic gradient descent
Diederik P Kingma and Jimmy Lei Ba · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Effective Approaches to Attention-based Neural Machine Translation
Minh-Thang Luong, Hieu Pham, and Christopher D. Manning · 2015
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
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Multimodal semantic learning from child-directed input
Angeliki Lazaridou, Grzegorz Chrupała, Raquel Fernández, and Marco Baroni · 2016
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Achieving open vocabulary neural machine translation with hybrid word-character models
Minh-Thang Luong and Christopher D Manning · 2016
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Never-Ending Learning
Tom Mitchell, William W. Cohen, E Hruschka, Partha Talukdar, B Yang, Justin Betteridge, Andrew Carlson, B Dalvi, Matt Gardner, Bryan Kisiel, J Krishnamurthy, Ni Lao, K Mazaitis, T Mohamed, N Nakashole, E Platanios, A Ritter, M Samadi, B Settles, R Wang, D Wijaya, A Gupta, X Chen, A Saparov, M Greaves, and J Welling · 2018
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Human few-shot learning of compositional instructions
Brenden M Lake, Tal Linzen, and Marco Baroni · 2019
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Approximating CNNs with bag-of-local-features models works suprisinlgy well on ImaeNet
Wieland Brendel and Matthias Bethge · 2019
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ImageNet-Trained CNNs are biased toward texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
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Lost in machine translation: A method to reduce meaning loss
Reuben Cohn-Gordon and Noah Goodman · 2019
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Infinite mixture prototypes for few-shot learning
Kelsey Allen, Evan Shelhamer, Hanul Shin, and Joshua Tenenbaum · 2019
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Compositional generalization through meta sequence-to-sequence learning
Brenden M Lake · 2019
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Which one is the dax? Achieving mutual exclusivity with neural networks
Kristina Gulordava, Thomas Brochhagen, and Gemma Boleda · 2020
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The role of developmental change and linguistic experience in the mutual exclusivity effect
Molly Lewis, Veronica Cristiano, Brenden M Lake, Tammy Kwan, and Michael C Frank · 2020
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