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Meta Learning automates the search for learning algorithms.
The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors
Linnainmaa, S · 1970
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Neural network model for a mechanism of pattern recognition unaffected by shift in position-neocognitron
Fukushima, K · 1979
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Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Schmidhuber, J · 1987
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Designing neural networks using genetic algorithms
Miller, G. F., Todd, P. M., and Hegde, S. U · 1989
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Turing computability with neural nets
Siegelmann, H. T. and Sontag, E. D · 1991
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Learning to control fast-weight memories: An alternative to recurrent nets
Schmidhuber, J · 1992
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An economics approach to hard computational problems
Huberman, B. A., Lukose, R. M., and Hogg, T · 1997
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Artificial life: An overview
Langton, C. G · 1997
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Shifting inductive bias with success-story algorithm, adaptive levin search, and incremental self-improvement
Schmidhuber, J., Zhao, J., and Wiering, M · 1997
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Learning to learn using gradient descent
Hochreiter, S., Younger, A. S., and Conwell, P. R · 2001
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A novel generative encoding for exploiting neural network sensor and output geometry
D’Ambrosio, D. B. and Stanley, K. O · 2007
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Algorithm portfolio selection as a bandit problem with unbounded losses
Gagliolo, M. and Schmidhuber, J · 2011
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Rl 2 : Fast reinforcement learning via slow reinforcement learning
Duan, Y., Schulman, J., Chen, X., Bartlett, P. L., Sutskever, I., and Abbeel, P · 2016
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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
Cited alongside, same era.
Improving generalization in meta reinforcement learning using learned objectives
Kirsch, L., van Steenkiste, S., and Schmidhuber, J · 2019
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Badger: Learning to (learn [learning algorithms] through multi-agent communication)
Rosa, M., Afanasjeva, O., Andersson, S., Davidson, J., Guttenberg, N., Hlubuček, P., Poliak, M., Vítku, J., and Feyereisl, J · 2019
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Experiment Tracking with Weights and Biases, 2020
Biewald, L · 2020
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Differentiable plasticity: training plastic neural networks with backpropagation
Miconi, T., Stanley, K., and Clune, J · 2018
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Neural architecture search: A survey
Elsken, T., Metzen, J. H., and Hutter, F · 2019
Cited alongside, same era.
Meta-learning with warped gradient descent
Flennerhag, S., Rusu, A. A., Pascanu, R., Visin, F., Yin, H., and Hadsell, R · 2019
Cited alongside, same era.
On decreasing the ratio between learning complexity and number of time-varying variables in fully recurrent nets
Schmidhuber, J
Cited in the paper.
A ‘self-referential’weight matrix
Schmidhuber, J
Cited in the paper.
Kirsch, L. and Schmidhuber, J · 2020
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Learning associative inference using fast weight memory
Schlag, I., Munkhdalai, T., and Schmidhuber, J · 2020
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Flennerhag, S., Schroecker, Y., Zahavy, T., van Hasselt, H., Silver, D., and Singh, S · 2021
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A modern self-referential weight matrix that learns to modify itself
Irie, K., Schlag, I., Csordás, R., and Schmidhuber, J · 2021
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Introducing symmetries to black box meta reinforcement learning
Kirsch, L., Flennerhag, S., van Hasselt, H., Friesen, A., Oh, J., and Chen, Y · 2021
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