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Many concepts have been proposed for meta learning with neural networks (NNs), e.g., NNs that learn to reprogram fast weights, Hebbian plasticity, learned learning rules, and meta recurrent NNs.
The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors
S. Linnainmaa · 1970
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Evolutionary principles in self-referential learning. Diploma thesis, Institut für Informatik, Technische Universität München, 1987
J. Schmidhuber · 1987
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Learning to Control Fast-Weight Memories: An Alternative to Recurrent Nets
J. Schmidhuber · 1991
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Neural networks with external memeory stack that learn context-free grammars from examples
G. Z. Sun · 1991
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On the optimization of a synaptic learning rule
S. Bengio, Y. Bengio, J. Cloutier, and J. Gecsei · 1992
Earlier work this paper cites.
Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks
J. Schmidhuber · 1992
Earlier work this paper cites.
A connectionist symbol manipulator that discovers the structure of context-free languages
M. C. Mozer and S. Das · 1993
Earlier work this paper cites.
Discovering Problem Solutions with Low Kolmogorov Complexity and High Generalization Capability
J. Schmidhuber · 1994
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Evolving Virtual Creatures
Sims, Karl · 1994
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Encoding labeled graphs by labeling raam
A. Sperduti · 1994
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Long Short-Term Memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Learning to Forget: Continual Prediction with LSTM
F. A. Gers, J. Schmidhuber, and F. Cummins · 2000
Earlier work this paper cites.
Learning to learn using gradient descent
S. Hochreiter, A. S. Younger, and P. R. Conwell · 2001
Earlier work this paper cites.
Natural Evolution Strategies
D. Wierstra, T. Schaul, J. Peters, and J. Schmidhuber · 2008
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MNIST handwritten digit database
Y. LeCun, C. Cortes, and C. J. Burges · 2010
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Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio · 2014
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Cellular automata
E. F. Codd · 2014
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Learning to learn by gradient descent by gradient descent
M. Andrychowicz, M. Denil, S. G. Colmenarejo, M. W. Hoffman, D. Pfau, T. Schaul, B. Shillingford, and N. De Freitas · 2016
Earlier work this paper cites.
Using Fast Weights to Attend to the Recent Past
J. Ba, G. Hinton, V. Mnih, J. Z. Leibo, and C. Ionescu · 2016
Earlier work this paper cites.
RLˆ2: Fast Reinforcement Learning via Slow Reinforcement Learning
Y. Duan, J. Schulman, X. Chen, P. L. Bartlett, I. Sutskever, and P. Abbeel · 2016
Cited alongside, same era.
HyperNetworks
D. Ha, A. Dai, and Q. V. Le · 2016
Cited alongside, same era.
K. Li and J. Malik · 2016
Cited alongside, same era.
Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2016
Cited alongside, same era.
Meta-Learning with Memory-Augmented Neural Networks
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap · 2016
Cited alongside, same era.
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
D. Pathak, C. Lu, T. Darrell, P. Isola, and A. A. Efros · 2019
Later among the works it cites.
BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)
M. Rosa, O. Afanasjeva, S. Andersson, J. Davidson, N. Guttenberg, P. Hlubuček, M. Poliak, J. Vítku, and J. Feyereisl · 2019
Later among the works it cites.
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
E. Triantafillou, T. Zhu, V. Dumoulin, P. Lamblin, U. Evci, K. Xu, R. Goroshin, C. Gelada, K. Swersky, P.-A. Manzagol, and H. Larochelle · 2019
Later among the works it cites.
Meta-learning curiosity algorithms
F. Alet, M. F. Schneider, T. Lozano-Perez, and L. P. Kaelbling · 2020
Closest in time.
Experiment Tracking with Weights and Biases, 2020
L. Biewald · 2020
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J. X. Wang, Z. Kurth-Nelson, D. Tirumala, H. Soyer, J. Z. Leibo, R. Munos, C. Blundell, D. Kumaran, and M. Botvinick · 2016
Cited alongside, same era.
EMNIST: Extending MNIST to handwritten letters
G. Cohen, S. Afshar, J. Tapson, and A. V. Schaik · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
Cited alongside, same era.
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
T. Salimans, J. Ho, X. Chen, S. Sidor, and I. Sutskever · 2017
Cited alongside, same era.
Gated Fast Weights for On-The-Fly Neural Program Generation
I. Schlag and J. Schmidhuber · 2017
Cited alongside, same era.
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le, G. Hinton, and J. Dean · 2017
Cited alongside, same era.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
Cited alongside, same era.
K. Gregor · 2020
Closest in time.
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
W. Huang, I. Mordatch, and D. Pathak · 2020
Closest in time.
Improving Generalization in Meta Reinforcement Learning using Learned Objectives
L. Kirsch, S. van Steenkiste, and J. Schmidhuber · 2020
Closest in time.
L. Metz, N. Maheswaranathan, C. D. Freeman, B. Poole, and J. Sohl-Dickstein · 2020
Closest in time.
Growing neural cellular automata
A. Mordvintsev, E. Randazzo, E. Niklasson, and M. Levin · 2020
Closest in time.
Meta-Learning through Hebbian Plasticity in Random Networks
E. Najarro and S. Risi · 2020
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Discovering Reinforcement Learning Algorithms
J. Oh, M. Hessel, W. M. Czarnecki, Z. Xu, H. van Hasselt, S. Singh, and D. Silver · 2020
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MPLP: Learning a Message Passing Learning Protocol
E. Randazzo, E. Niklasson, and A. Mordvintsev · 2020
Closest in time.
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
E. Real, C. Liang, D. R. So, and Q. V. Le · 2020
Closest in time.
Cross-domain few-shot classification via learned feature-wise transformation
H.-Y. Tseng, H.-Y. Lee, J.-B. Huang, and M.-H. Yang · 2020
Closest in time.
A comprehensive survey on graph neural networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip · 2020
Closest in time.
Meta-Learning Bidirectional Update Rules
M. Sandler, M. Vladymyrov, A. Zhmoginov, N. Miller, A. Jackson, T. Madams, and others · 2021
Closest in time.
Learning Associative Inference Using Fast Weight Memory
I. Schlag, T. Munkhdalai, and J. Schmidhuber · 2021
Closest in time.
Growing 3d artefacts and functional machines with neural cellular automata
S. Sudhakaran, D. Grbic, S. Li, A. Katona, E. Najarro, C. Glanois, and S. Risi · 2021
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