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The objective of meta-learning is to exploit the knowledge obtained from observed tasks to improve adaptation to unseen tasks.
Distributed learning in non-convex environments – Part I: Agreement at a linear rate
Vlaski, S. and Sayed, A. H. (2019b) · 1907
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Improving federated learning personalization via model agnostic meta learning
Jiang, Y., Konecný, J., Rush, K., and Kannan, S. (2019) · 1909
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Evolutionary principles in self-referential learning. on learning how to learn: The meta-meta-meta…-hook
Schmidhuber, J. (1987) · 1987
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Learning a synaptic learning rule
Bengio, Y., Bengio, S., and Cloutier, J. (1991) · 1991
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On the optimization of a synaptic learning rule
Bengio, S., Bengio, Y., Cloutier, J., and Gecsei, J. (1992) · 1992
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Learning to control fast-weight memories: An alternative to dynamic recurrent networks
Schmidhuber, J. (1992) · 1992
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Personalized federated learning: A meta-learning approach
Fallah, A., Mokhtari, A., and Ozdaglar, A. (2020b) · 2002
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Theoretical convergence of multi-step model-agnostic meta-learning
Ji, K., Yang, J., and Liang, Y. (2020) · 2002
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Second-order guarantees in centralized, federated and decentralized nonconvex optimization
Vlaski, S. and Sayed, A. H. (2020) · 2003
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Fast linear iterations for distributed averaging
Xiao, L. and Boyd, S. (2003) · 2003
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From swarm intelligence to swarm robotics
Beni, G. (2004) · 2004
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Meta-learning in neural networks: A survey
Hospedales, T. M., Antoniou, A., Micaelli, P., and Storkey, A. J. (2020) · 2004
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Swarm robotics: From sources of inspiration to domains of application
Sahin, E. (2004) · 2004
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L., Kai Li, and Li Fei-Fei (2009) · 2009
Cited alongside, same era.
Distributed subgradient methods for multi-agent optimization
Nedic, A. and Ozdaglar, A. (2009) · 2009
Cited alongside, same era.
Robots for environmental monitoring: Significant advancements and applications
Dunbabin, M. and Marques, L. (2012) · 2012
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2015) · 2015
Cited alongside, same era.
Siamese neural networks for one-shot image recognition
Koch, G., Zemel, R., and Salakhutdinov, R. (2015) · 2015
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S. (2017) · 2017
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Meta-SGD: Learning to learn quickly for few shot learning
Li, Z., Zhou, F., Chen, F., and Li, H. (2017) · 2017
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Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent
Lian, X., Zhang, C., Zhang, H., Hsieh, C.-J., Zhang, W., and Liu, J. (2017) · 2017
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Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H. (2017) · 2017
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Federated meta-learning with fast convergence and efficient communication
Chen, F., Luo, M., Dong, Z., Li, Z., and He, X. (2018) · 2018
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Probabilistic model-agnostic meta-learning
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Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2015) · 2015
Cited alongside, same era.
Learning to learn by gradient descent by gradient descent
Andrychowicz, M., Denil, M., Gómez, S., Hoffman, M. W., Pfau, D., Schaul, T., Shillingford, B., and de Freitas, N. (2016) · 2016
Cited alongside, same era.
Proximal multitask learning over networks with sparsity-inducing coregularization
Nassif, R., Richard, C., Ferrari, A., and Sayed, A. H. (2016) · 2016
Cited alongside, same era.
Meta-learning with memory-augmented neural networks
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T. (2016) · 2016
Cited alongside, same era.
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., and Wierstra, D. (2016) · 2016
Cited alongside, same era.
On the convergence of decentralized gradient descent
Yuan, K., Ling, Q., and Yin, W. (2016) · 2016
Cited alongside, same era.
Finn, C., Xu, K., and Levine, S. (2018) · 2018
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On first-order meta-learning algorithms
Nichol, A., Achiam, J., and Schulman, J. (2018) · 2018
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Provable guarantees for gradient-based meta-learning
Balcan, M.-F., Khodak, M., and Talwalkar, A. (2019) · 2019
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Adaptive gradient-based meta-learning methods
Khodak, M., Balcan, M.-F., and Talwalkar, A. (2019) · 2019
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MetaPred: Meta-learning for clinical risk prediction with limited patient electronic health records
Zhang, X. S., Tang, F., Dodge, H. H., Zhou, J., and Wang, F. (2019) · 2019
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Rapid learning or feature reuse? Towards understanding the effectiveness of MAML
Raghu, A., Raghu, M., Bengio, S., and Vinyals, O. (2020) · 2020
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No-regret non-convex online meta-learning
Zhuang, Z., Wang, Y., Yu, K., and Lu, S. (2020) · 2020
Closest in time.