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How can deep learning systems flexibly reuse their knowledge? Toward this goal, we propose a new class of challenges, and a class of architectures that can solve them.
Using Fast Weights to Deblur Old Memories
Hinton, G. E. and Plaut, D. C. (1982) · 1987
Earlier work this paper cites.
Catastrophic interference in connectionist networks: The sequential learning problem
McCloskey, M. and Cohen, N. J. (1989) · 1989
Earlier work this paper cites.
The Cognizer’s Innards: A Psychological and Philosophical Perspective on the Development of Thought
Clark, A. and Karmiloff-Smith, A. (1993) · 1993
Earlier work this paper cites.
Learning and connectionist representations
Rumelhart, D. E. and Todd, P. M. (1993) · 1993
Earlier work this paper cites.
Zero-data learning of new tasks
Larochelle, H., Erhan, D., and Bengio, Y. (2008) · 2002
Earlier work this paper cites.
Global workspace theory of consciousness: Toward a cognitive neuroscience of human experience
Baars, B. J. (2005) · 2005
Earlier work this paper cites.
Visualizing Data using t-SNE
Laurens van der Maaten and Hinton, G. (2008) · 2008
Earlier work this paper cites.
Zero-shot learning through cross-modal transfer
Socher, R., Ganjoo, M., Manning, C. D., and Ng, A. Y. (2013) · 2013
Earlier work this paper cites.
Neural Programmer-Interpreters
Reed, S. and de Freitas, N. (2015) · 2015
Earlier work this paper cites.
An embarrassingly simple approach to zero-shot learning
Romera-Paredes, B. and Torr, P. H. (2015) · 2015
Earlier work this paper cites.
Using Fast Weights to Attend to the Recent Past
Ba, J., Hinton, G., Mnih, V., Leibo, J. Z., and Ionescu, C. (2016) · 2016
Earlier work this paper cites.
RL$ˆ2$: Fast Reinforcement Learning via Slow Reinforcement Learning
Duan, Y., Schulman, J., Chen, X., Bartlett, P. L., Sutskever, I., and Abbeel, P. (2016) · 2016
Earlier work this paper cites.
Hybrid computing using a neural network with dynamic external memory
Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., Gómez Colmenarejo, S., Grefenstette, E., Ramalho, T., Agapiou, J., Badia, A. P., Moritz Hermann, K., Zwols, Y., Ostrovski, G., Cain, A., King, H., Summerfield, C., Blunsom, P., Kavukcuoglu, K., and Hassabis, D. (2016) · 2016
Earlier work this paper cites.
HyperNetworks
Ha, D., Dai, A., and Le, Q. V. (2016) · 2016
Earlier work this paper cites.
Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y., Chen, Z., Thorat, N., Viégas, F., Wattenberg, M., Corrado, G., Hughes, M., and Dean, J. (2016) · 2016
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., and Hadsell, R. (2016) · 2016
Earlier work this paper cites.
What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated
Kumaran, D., Hassabis, D., and McClelland, J. L. (2016) · 2016
Earlier work this paper cites.
Progressive neural networks
Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R. (2016) · 2016
Earlier work this paper cites.
Meta-Learning with Memory-Augmented Neural Networks
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T. (2016) · 2016
Earlier work this paper cites.
A Deep Hierarchical Approach to Lifelong Learning in Minecraft
Tessler, C., Givony, S., Zahavy, T., Mankowitz, D. J., and Mannor, S. (2016) · 2016
Earlier work this paper cites.
Matching Networks for One Shot Learning
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., and Wierstra, D. (2016) · 2016
Cited alongside, same era.
Learning to reinforcement learn
Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D., and Botvinick, M. (2016) · 2016
Cited alongside, same era.
Neural Architecture Search with Reinforcement Learning
Zoph, B. and Le, Q. V. (2016) · 2016
Cited alongside, same era.
Deep Compositional Question Answering with Neural Module Networks
Andreas, J., Rohrbach, M., Darrell, T., and Klein, D. (2017) · 2017
Cited alongside, same era.
Universal Successor Features Approximators
Borsa, D., Quan, J., Mankowitz, D., Hasselt, H. V., Silver, D., and Schaul, T. (2019) · 2017
Cited alongside, same era.
PathNet: Evolution Channels Gradient Descent in Super Neural Networks
Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A. A., Pritzel, A., and Wierstra, D. (2017) · 2017
Memory-based parameter Adaptation
Sprechmann, P., Jayakumar, S. M., Rae, J. W., Pritzel, A., Uria, B., Vinyals, O., Hassabis, D., Pascanu, R., and Blundell, C. (2018) · 2018
Later among the works it cites.
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
Stadie, B. C., Yang, G., Houthooft, R., Chen, X., Duan, Y., Wu, Y., Abbeel, P., and Sutskever, I. (2018) · 2018
Later among the works it cites.
Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly
Xian, Y., Lampert, C. H., Schiele, B., and Akata, Z. (2018) · 2018
Later among the works it cites.
Graph HyperNetworks for neural architecture search
Zhang, C., Ren, M., Urtasun, R., Advanced, U., and Group, T. (2019) · 2018
Later among the works it cites.
Task2Vec: Task Embedding for Meta-Learning
Achille, A., Lam, M., Tewari, R., Ravichandran, A., Maji, S., Fowlkes, C., Soatto, S., and Perona, P. (2019) · 2019
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Selfless sequential learning
Aljundi, R., Rohrbach, M., and Tuytelaars, T. (2019) · 2019
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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., and Levine, S. (2017) · 2017
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Grounded Language Learning in a Simulated 3D World
Hermann, K. M., Hill, F., Green, S., Wang, F., Faulkner, R., Soyer, H., Szepesvari, D., Czarnecki, W. M., Jaderberg, M., Teplyashin, D., Wainwright, M., Apps, C., and Hassabis, D. (2017) · 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) · 2017
Cited alongside, same era.
Transfer Reinforcement Learning with Shared Dynamics
Laroche, R. and Barlier, M. (2017) · 2017
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Meta Networks
Munkhdalai, T. and Yu, H. (2017) · 2017
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Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning
Oh, J., Singh, S., Lee, H., and Kohli, P. (2017) · 2017
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Reinforcement Learning , Fast and Slow
Botvinick, M., Ritter, S., Wang, J. X., Kurth-nelson, Z., Blundell, C., and Hassabis, D. (2019) · 2019
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Learnable Embedding Space for Efficient Neural Architecture Compression
Cao, S., Wang, X., and Kitani, K. M. (2019) · 2019
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Automatically Composing Representation Transformations as a Means for Generalization
Chang, M. B., Gupta, A., Levine, S., and Griffiths, T. L. (2019) · 2019
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Guiding policies with language via meta-learning
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Diversity is all you need: learning skills without a reward function
Eysenbach, B., Gupta, A., Ibarz, J., and Levine, S. (2019) · 2019
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Online Meta-Learning
Finn, C., Rajeswaran, A., Kakade, S., and Levine, S. (2019) · 2019
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Unsupervised Learning Via Meta-Learning
Hsu, K., Levine, S., and Finn, C. (2019) · 2019
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Overcoming catastrophic forgetting for continual learning via model adaptation
Hu, W., Lin, Z., Liu, B., Tao, C., Tao, Z., Zhao, D., and Yan, R. (2019) · 2019
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An analytic theory of generalization dynamics and transfer learning in deep linear networks
Lampinen, A. K. and Ganguli, S. (2019) · 2019
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LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
Li, H., Dong, W., Mei, X., Ma, C., Huang, F., and Hu, B.-G. (2019) · 2019
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Zero-Shot Task Transfer
Pal, A. and Balasubramanian, V. N. (2019) · 2019
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Meta-Learning with Latent Embedding Optimization
Rusu, A. A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., and Hadsell, R. (2019) · 2019
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Hierarchically Structured Meta-learning
Yao, H., Wei, Y., Huang, J., and Li, Z. (2019) · 2019
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