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Whether neural networks can learn abstract reasoning or whether they merely rely on superficial statistics is a topic of recent debate.
Raven’s progressive matrices
Raven, J. C. et al · 1938
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Practice and coaching on iq tests: Quite a lot of g
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Improving fluid intelligence with training on working memory
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Comparing machines and humans on a visual categorization test
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Intriguing properties of neural networks
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Recurrent neural network regularization
Zaremba, W., Sutskever, I., and Vinyals, O · 2014
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
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Automatic generation of raven’s progressive matrices
Wang, K. and Su, Z · 2015
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Modular multitask reinforcement learning with policy sketches
Andreas, J., Klein, D., and Levine, S · 2016
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My computer is an honor student—but how intelligent is it? standardized tests as a measure of ai
Clark, P. and Etzioni, O · 2016
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Scan: learning abstract hierarchical compositional visual concepts
Higgins, I., Sonnerat, N., Matthey, L., Pal, A., Burgess, C. P., Botvinick, M., Hassabis, D., and Lerchner, A · 2017
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Hoshen, D. and Werman, M · 2017
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Measuring the tendency of cnns to learn surface statistical regularities
Jo, J. and Bengio, Y · 2017
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., and Girshick, R · 2017
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Garnelo, M., Arulkumaran, K., and Shanahan, M · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2016
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Beyond the turing test
Marcus, G., Rossi, F., and Veloso, M · 2016
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Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2016
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Botvinick, M., Barrett, D., Battaglia, P., de Freitas, N., Kumaran, D., Leibo, J., Lillicrap, T., Modayil, J., Mohamed, S., Rabinowitz, N., et al · 2017
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Lake, B. M. and Baroni, M · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Ling, W., Yogatama, D., Dyer, C., and Blunsom, P · 2017
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Modeling visual problem solving as analogical reasoning
Lovett, A. and Forbus, K · 2017
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An empirical evaluation of visual question answering for novel objects
Ramakrishnan, S. K., Pal, A., Sharma, G., and Mittal, A · 2017
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Discovering objects and their relations from entangled scene representations
Raposo, D., Santoro, A., Barrett, D., Pascanu, R., Lillicrap, T., and Battaglia, P · 2017
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A simple neural network module for relational reasoning
Santoro, A., Raposo, D., Barrett, D. G., Malinowski, M., Pascanu, R., Battaglia, P., and Lillicrap, T · 2017
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Deep learning: A critical appraisal
Marcus, G · 2018
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