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Compositionality is a key strategy for addressing combinatorial complexity and the curse of dimensionality.
A meta-mdp approach to exploration for lifelong reinforcement learning
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A computational model of action selection in the basal ganglia. i. a new functional anatomy
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Information transfer in entrained cortical neurons
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A mechanism for cognitive dynamics: neuronal communication through neuronal coherence
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Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research
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Pathnet: Evolution channels gradient descent in super neural networks
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Stochastic neural networks for hierarchical reinforcement learning
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Shifting the spotlight of attention: evidence for discrete computations in cognition
Buschman, T. J. and E. K. Miller 2010 · 2010
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Conditional routing of information to the cortex: A model of the basal ganglia’s role in cognitive coordination
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Conjugate markov decision processes
Thomas, P. S. and A. G. Barto 2011 · 2011
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Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., N. Léonard, and A. C. Courville 2013 · 2013
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Low-rank approximations for conditional feedforward computation in deep neural networks
Davis, A. and I. Arel 2013 · 2013
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Deep networks with internal selective attention through feedback connections
Stollenga, M. F., J. Masci, F. Gomez, and J. Schmidhuber 2014 · 2014
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Rosenbaum, C., T. Klinger, and M. Riemer 2017 · 2017
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Sluice networks: Learning what to share between loosely related tasks
Ruder, S., J. Bingel, I. Augenstein, and A. Søgaard 2017 · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Shazeer, N., A. Mirhoseini, K. Maziarz, A. Davis, Q. V. Le, G. E. Hinton, and J. Dean 2017 · 2017
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REBAR: low-variance, unbiased gradient estimates for discrete latent variable models
Tucker, G., A. Mnih, C. J. Maddison, and J. Sohl-Dickstein 2017 · 2017
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Neural architecture search with reinforcement learning
Zoph, B. and Q. V. Le 2017 · 2017
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Alet, F., T. Lozano-Pérez, and L. P. Kaelbling 2018 · 2018
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Understanding and simplifying one-shot architecture search
Bender, G., P.-J. Kindermans, B. Zoph, V. Vasudevan, and Q. Le 2018 · 2018
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Diversity is all you need: Learning skills without a reward function
Eysenbach, B., A. Gupta, J. Ibarz, and S. Levine 2018 · 2018
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Backpropagation through the void: Optimizing control variates for black-box gradient estimation
Grathwohl, W., D. Choi, Y. Wu, G. Roeder, and D. Duvenaud 2018 · 2018
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Learning an embedding space for transferable robot skills
Hausman, K., J. T. Springenberg, Z. Wang, N. Heess, and M. Riedmiller 2018 · 2018
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A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy
Kell, A. J., D. L. Yamins, E. N. Shook, S. V. Norman-Haignere, and J. H. McDermott 2018 · 2018
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Modular networks: Learning to decompose neural computation
Kirsch, L., J. Kunze, and D. Barber 2018 · 2018
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Evolutionary architecture search for deep multitask networks
Liang, J. Z., E. Meyerson, and R. Miikkulainen 2018 · 2018
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Progressive neural architecture search
Liu, C., B. Zoph, M. Neumann, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy 2018 · 2018
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Efficient neural architecture search via parameter sharing
Pham, H., M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean 2018 · 2018
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Learning abstract options
Riemer, M., M. Liu, and G. Tesauro 2018 · 2018
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Recursive routing networks: Learning to compose modules for language understanding
Cases, I., C. Rosenbaum, M. Riemer, A. Geiger, T. Klinger, A. Tamkin, O. Li, S. Agarwal, J. D. Greene, D. Jurafsky, C. Potts, and L. Karttunen 2019 · 2019
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Automatically composing representation transformations as a means for generalization
Chang, M., A. Gupta, S. Levine, and T. L. Griffiths 2019 · 2019
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Diversity and depth in per-example routing models
Ramachandran, P. and Q. V. Le 2019 · 2019
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Learning to learn without forgetting by maximizing transfer and minimizing interference
Riemer, M., I. Cases, R. Ajemian, M. Liu, I. Rish, Y. Tu, and G. Tesauro 2019 · 2019
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Selecting computations: Theory and applications
Hay, N., S. Russell, D. Tolpin, and S. E. Shimony 2014 · 2048
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