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In order to meet the diverse challenges in solving many real-world problems, an intelligent agent has to be able to dynamically construct a model of its environment.
The magical number seven, plus or minus two: Some limits on our capacity for processing information
Miller, G. A · 1956
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Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments
Schmidhuber, J · 1990
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Tensor product variable binding and the representation of symbolic structures in connectionist systems
Smolensky, P · 1990
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Learning to generate artificial fovea trajectories for target detection
Schmidhuber, J. and Huber, R · 1991
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Learning context-free grammars: Capabilities and limitations of a neural network with an external stack memory
Das, S., Giles, C., and Sun, G · 1992
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Learning to control fast-weight memories: An alternative to recurrent nets
Schmidhuber, J · 1992
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A connectionist symbol manipulator that discovers the structure of context-free languages
Mozer, M. C. and Das, S · 1993
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On decreasing the ratio between learning complexity and number of time-varying variables in fully recurrent nets
Schmidhuber, J · 1993
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On being systematically connectionist
Niklasson, L. F. and van Gelder, T · 1994
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Binding in models of perception and brain function
von der Malsburg, C · 1995
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Rethinking infant knowledge: Toward an adaptive process account of successes and failures in object permanence tasks
Munakata, Y., McClelland, J. L., Johnson, M. H., and Siegler, R. S · 1997
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Simple synchrony networks: Learning to parse natural language with temporal synchrony variable binding
Lane, P. C. and Henderson, J. B · 1998
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Solutions to the binding problem: Progress through controversy and convergence
Treisman, A · 1999
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Connectionist variable binding
Browne, A. and Sun, R · 2000
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A symbolic-connectionist theory of relational inference and generalization
Hummel, J. E. and Holyoak, K. J · 2003
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Core knowledge
Spelke, E. S. and Kinzler, K. D · 2007
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An object-oriented representation for efficient reinforcement learning
Diuk, C., Cohen, A., and Littman, M. L · 2008
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The discovery of structural form
Kemp, C. and Tenenbaum, J. B · 2008
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Neuronal synchrony in complex-valued deep networks
Reichert, D. P. and Serre, T · 2013
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Neural Turing machines
Graves, A., Wayne, G., and Danihelka, I · 2014
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Recurrent models of visual attention
Leike, J., Martic, M., Krakovna, V., Ortega, P. A., Everitt, T., Lefrancq, A., Orseau, L., and Legg, S · 2017
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Poincaré embeddings for learning hierarchical representations
Nickel, M. and Kiela, D · 2017
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A simple neural network module for relational reasoning
Santoro, A., Raposo, D., Barrett, D. G. T., Malinowski, M., Pascanu, R., Battaglia, P. W., and Lillicrap, T · 2017
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Human learning in Atari
Tsividis, P. A., Pouncy, T., Xu, J. L., Tenenbaum, J. B., and Gershman, S. J · 2017
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Relational inductive biases, deep learning, and graph networks
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., Gulcehre, C., Song, H. F., Ballard, A., Gilmer, J., Dahl, G. E., Vaswani, A., Allen, K., Nash, C., Langston, V., Dyer, C., Heess, N., Wierstra, D., Kohli, P., Botvinick, M., Vinyals, O., Li, Y., and Pascanu, R · 2018
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Mnih, V., Heess, N., Graves, A., and Kavukcuoglu, K · 2014
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Neural programmer-interpreters
Reed, S. and de Freitas, N · 2015
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Interaction networks for learning about objects, relations and physics
Battaglia, P. W., Pascanu, R., Lai, M., and Rezende, D. J · 2016
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Attend, infer, repeat: Fast scene understanding with generative models
Eslami, S. M. A., Heess, N., Weber, T., Tassa, Y., Szepesvari, D., Kavukcuoglu, K., and Hinton, G. E · 2016
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Hybrid computing using a neural network with dynamic external memory
Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., Colmenarejo, S. G., Grefenstette, E., Ramalho, T., Agapiou, J., Badia, A. P., Hermann, K. M., Zwols, Y., Ostrovski, G., Cain, A., King, H., Summerfield, C., Blunsom, P., Kavukcuoglu, K., and Hassabis, D · 2016
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Neural expectation maximization
Greff, K., van Steenkiste, S., and Schmidhuber, J · 2017
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End-to-end differentiable physics for learning and control
de Avila Belbute-Peres, F., Smith, K., Allen, K., Tenenbaum, J., and Kolter, J. Z · 2018
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An introduction to deep reinforcement learning
François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., Pineau, J., et al · 2018
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Recurrent world models facilitate policy evolution
Ha, D. and Schmidhuber, J · 2018
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Sequential attend, infer, repeat: Generative modelling of moving objects
Kosiorek, A., Kim, H., Teh, Y. W., and Posner, I · 2018
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Learning to reason with third order tensor products
Schlag, I. and Schmidhuber, J · 2018
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Relational neural expectation maximization: Unsupervised discovery of objects and their interactions
van Steenkiste, S., Chang, M., Greff, K., and Schmidhuber, J · 2018
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Multi-object representation learning with iterative variational inference
Greff, K., Kaufman, R. L., Kabra, R., Watters, N., Burgess, C. P., Zoran, D., Matthey, L., Botvinick, M., and Lerchner, A · 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
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