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Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks.
The ishihara test for color blindness
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Probabilistic models of cognition: exploring representations and inductive biases
Thomas L Griffiths, Nick Chater, Charles Kemp, Amy Perfors, and Joshua B Tenenbaum · 2010
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Letting structure emerge: Connectionist and dynamical systems approaches to cognition
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A probabilistic approach to learning a visually grounded language model through human-robot interaction
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Eye spy: Improving vision through dialog
Adam Vogel, Karthik Raghunathan, and Dan Jurafsky · 2010
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How to grow a mind: Statistics, structure, and abstraction
Joshua B Tenenbaum, Charles Kemp, Thomas L Griffiths, and Noah D Goodman · 2011
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Algorithms for hyper-parameter optimization
James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Probabilistic labeling for efficient referential grounding based on collaborative discourse
Changsong Liu, Lanbo She, Rui Fang, and Joyce Chai · 2014
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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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Human Learning, eBook
Jeanne Ellis Ormrod · 2016
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Learning to navigate in complex environments
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Loss is its own reward: Self-supervision for reinforcement learning
Evan Shelhamer, Parsa Mahmoudieh, Max Argus, and Trevor Darrell · 2016
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Reinforcement learning with unsupervised auxiliary tasks
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How language programs the mind
Gary Lupyan and Benjamin Bergen · 2016
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Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
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Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Program synthesis
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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Learning with latent language
Jacob Andreas, Dan Klein, and Sergey Levine · 2018
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Grounding language for transfer in deep reinforcement learning
Karthik Narasimhan, Regina Barzilay, and Tommi Jaakkola · 2018
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Meta-learning of sequential strategies
Pedro A Ortega, Jane X Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alex Pritzel, Pablo Sprechmann, et al · 2019
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Inductive biases for deep learning of higher-level cognition
Anirudh Goyal and Yoshua Bengio · 2020
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Meta-learning in neural networks: A survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2020
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Shaping visual representations with language for few-shot classification
Jesse Mu, Percy Liang, and Noah Goodman · 2020
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Learning compositional rules via neural program synthesis
Maxwell Nye, Armando Solar-Lezama, Josh Tenenbaum, and Brenden M Lake · 2020
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Neil Rabinowitz, Frank Perbet, Francis Song, Chiyuan Zhang, SM Ali Eslami, and Matthew Botvinick · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Prefrontal cortex as a meta-reinforcement learning system
Jane X Wang, Zeb Kurth-Nelson, Dharshan Kumaran, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Demis Hassabis, and Matthew Botvinick · 2018
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Grounding referring expressions in images by variational context
Hanwang Zhang, Yulei Niu, and Shih-Fu Chang · 2018
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Guided feature transformation (gft): A neural language grounding module for embodied agents
Haonan Yu, Xiaochen Lian, Haichao Zhang, and Wei Xu · 2018
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Learning to infer graphics programs from hand-drawn images
Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, and Josh Tenenbaum · 2018
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Subjective randomness as statistical inference
Thomas L Griffiths, Dylan Daniels, Joseph L Austerweil, and Joshua B Tenenbaum · 2018
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Learning abstract structure for drawing by efficient motor program induction
Lucas Tian, Kevin Ellis, Marta Kryven, and Josh Tenenbaum · 2020
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Universal linguistic inductive biases via meta-learning
R Thomas McCoy, Erin Grant, Paul Smolensky, Thomas L Griffiths, and Tal Linzen · 2020
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Gibbs sampling with people
Peter Harrison, Raja Marjieh, Federico Adolfi, Pol van Rijn, Manuel Anglada-Tort, Ofer Tchernichovski, Pauline Larrouy-Maestri, and Nori Jacoby · 2020
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An mturk crisis? shifts in data quality and the impact on study results
Michael Chmielewski and Sarah C Kucker · 2020
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Meta-learning of structured task distributions in humans and machines
Sreejan Kumar, Ishita Dasgupta, Jonathan Cohen, Nathaniel Daw, and Thomas Griffiths · 2021
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Leveraging language to learn program abstractions and search heuristics
Catherine Wong, Kevin M Ellis, Joshua Tenenbaum, and Jacob Andreas · 2021
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Tell me why! – explanations support learning of relational and causal structure
Andrew K Lampinen, Nicholas A Roy, Ishita Dasgupta, Stephanie CY Chan, Allison C Tam, James L McClelland, Chen Yan, Adam Santoro, Neil C Rabinowitz, Jane X Wang, et al · 2021
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Dreamcoder: bootstrapping inductive program synthesis with wake-sleep library learning
Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sablé-Meyer, Lucas Morales, Luke Hewitt, Luc Cary, Armando Solar-Lezama, and Joshua B Tenenbaum · 2021
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Learning to communicate about shared procedural abstractions
William P McCarthy, Robert D Hawkins, Haoliang Wang, Cameron Holdaway, and Judith E Fan · 2021
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Multimodal few-shot learning with frozen language models
Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Semantic supervision: Enabling generalization over output spaces
Austin W Hanjie, Ameet Deshpande, and Karthik Narasimhan · 2022
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Identifying concept libraries from language about object structure
Catherine Wong, William P McCarthy, Gabriel Grand, Yoni Friedman, Joshua B Tenenbaum, Jacob Andreas, Robert D Hawkins, and Judith E Fan · 2022
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Intra-agent speech permits zero-shot task acquisition
Chen Yan, Federico Carnevale, Petko Georgiev, Adam Santoro, Aurelia Guy, Alistair Muldal, Chia-Chun Hung, Josh Abramson, Timothy Lillicrap, and Gregory Wayne · 2022
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