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With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations with an explicitly relational structure from raw pixel data.
Generality in artificial intelligence
McCarthy, J · 1987
Earlier work this paper cites.
The symbol grounding problem
Harnad, S · 1990
Earlier work this paper cites.
Introduction to Statistical Relational Learning
Getoor, L. and Taskar, B. (eds.) · 2007
Earlier work this paper cites.
Curriculum learning
Bengio, Y., Louradour, J., Collobert, R., and Weston, J · 2009
Earlier work this paper cites.
Artificial Intelligence: A Modern Approach
Russell, S. and Norvig, P · 2009
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Learning structured embeddings of knowledge bases
Bordes, A., Weston, J., Collobert, R., and Bengio, Y · 2011
Earlier work this paper cites.
Deep learning of representations for unsupervised and transfer learning
Bengio, Y · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
Earlier work this paper cites.
Reasoning with neural tensor networks for knowledge base completion
Socher, R., Chen, D., Manning, C., and Ng, A · 2013
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
Battaglia, P. W., Pascanu, R., Lai, M., Rezende, D. J., and Kavukcuoglu, K · 2016
Earlier work this paper cites.
Towards deep symbolic reinforcement learning
Garnelo, M., Arulkumaran, K., and Shanahan, M · 2016
Earlier work this paper cites.
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. B · 2017
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Schema networks: Zero-shot transfer with a generative causal model of intuitive physics
Kansky, K., Silver, T., Mély, D. A., Eldawy, M., Lázaro-Gredilla, M., Lou, X., Dorfman, N., Sidor, S., Phoenix, S., and George, D · 2017
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Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J · 2017
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Imagination-augmented agents for deep reinforcement learning
Racanière, S., Weber, T., Reichert, D. P., Buesing, L., Guez, A., Rezende, D., Badia, A. P., Vinyals, O., Heess, N., Li, Y., Pascanu, R., Battaglia, P., Hassabis, D., Silver, D., and Wierstra, D · 2017
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End-to-end differentiable proving
Rocktäschel, T. and Riedel, S · 2017
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Recurrent relational networks
Palm, R. B., Paquet, U., and Winter, O · 2018
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Relational recurrent neural networks
Santoro, A., Faulkner, R., Raposo, D., Jack, R., Chrzanowski, M., Weber, T., Vinyals, O., Pascanu, R., and Lillicrap, T · 2018
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Progress & compress: A scalable framework for continual learning
Schwarz, J., Czarnecki, W., Luketina, J., Grabska-Barwinska, A., Teh, Y. W., Pascanu, R., and Hadsell, R · 2018
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Non-local neural networks
Wang, X., Girshick, R., Gupta, A., and He, K · 2018
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Unsupervised grounding of plannable first-order logic representation from images
Asai, M · 2019
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Universal transformers
Dehghani, M., Gouws, S., Vinyals, O., Uszkoreit, J., and Kaiser, L · 2019
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Santoro, A., Raposo, D., Barrett, D. G., Malinowski, M., Pascanu, R., Battaglia, P., and Lillicrap, T · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
IMPALA: Scalable distributed deep-RL with importance weighted actor-learner architectures
Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Doron, Y., Firoiu, V., Harley, T., Dunning, I., Legg, S., and Kavukcuoglu, K · 2018
Cited alongside, same era.
Learning explanatory rules from noisy data
Evans, R. and Grefenstette, E · 2018
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Scan: Learning hierarchical compositional visual concepts
Higgins, I., Sonnerat, N., Matthey, L., Pal, A., Burgess, C. P., Bosnjak, M., Shanahan, M., Botvinick, M., Hassabis, D., and Lerchner, A · 2018
Cited alongside, same era.
Deep learning: a critical appraisal
Marcus, G · 2018
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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
Cited in the paper.
Neural logic machines
Dong, H., Mao, J., Lin, T., Wang, C., Li, L., and Zhou, D · 2019
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Reconciling deep learning with symbolic artificial intelligence: representing objects and relations
Garnelo, M. and Shanahan, M · 2019
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Towards interpretable reinforcement learning using attention augmented agents
Mott, A., Zoran, D., Chrzanowski, M., Wierstra, D., and Rezende, D · 2019
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The Promise of Artificial Intelligence: Reckoning and Judgment
Smith, B. C · 2019
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Deep reinforcement learning with relational inductive biases
Zambaldi, V., Raposo, D., Santoro, A., Bapst, V., Li, Y., Babuschkin, I., Tuyls, K., Reichert, D., Lillicrap, T., Lockhart, E., Shanahan, M., Langston, V., Pascanu, R., Botvinick, M., Vinyals, O., and Battaglia, P · 2019
Closest in time.