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Many IoT applications at the network edge demand intelligent decisions in a real-time manner.
J. Schmidhuber, “Evolutionary principles in self-referential learning,” Ph.D. dissertation, Technische Universität München, 1987
1987
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner et al. , “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
T. Evgeniou and M. Pontil, “Regularized multi–task learning,” in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 2004, pp. 109–117
2004
Earlier work this paper cites.
A. Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” CS224N Project Report, Stanford , vol. 1, no. 12, p. 2009, 2009
2009
Earlier work this paper cites.
A. Shapiro, D. Dentcheva, and A. Ruszczyński, Lectures on stochastic programming: modeling and theory . SIAM, 2009
2009
Earlier work this paper cites.
J. Pennington, R. Socher, and C. Manning, “Glove: Global vectors for word representation,” in Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) , 2014, pp. 1532–1543
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2016
Earlier work this paper cites.
S. Ravi and H. Larochelle, “Optimization as a model for few-shot learning,” in International conference on learning representations , 2017
2017
Earlier work this paper cites.
V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar, “Federated multi-task learning,” in Advances in Neural Information Processing Systems , 2017, pp. 4424–4434
2017
Cited alongside, same era.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 2017, pp. 1126–1135
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Later among the works it cites.
R. Volpi, H. Namkoong, O. Sener, J. C. Duchi, V. Murino, and S. Savarese, “Generalizing to unseen domains via adversarial data augmentation,” in Advances in Neural Information Processing Systems , 2018, pp. 5334–5344
2018
Later among the works it cites.
X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, and M. Chen, “In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning,” IEEE Network , 2019
2019
Later among the works it cites.
Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang, “Edge intelligence: Paving the last mile of artificial intelligence with edge computing,” Proceedings of the IEEE , vol. 107, no. 8, pp. 1738–1762, 2019
2019
Later among the works it cites.
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2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
R. Edmunds, N. Golmant, V. Ramasesh, P. Kuznetsov, P. Patil, and R. Puri, “Transferability of adversarial attacks in model-agnostic meta-learning.”
Cited in the paper.
2019
Later among the works it cites.
S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “Adaptive federated learning in resource constrained edge computing systems,” IEEE Journal on Selected Areas in Communications , vol. 37, no. 6, pp. 1205–1221, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
J. Blanchet and K. Murthy, “Quantifying distributional model risk via optimal transport,” Mathematics of Operations Research , vol. 44, no. 2, pp. 565–600, 2019
2019
Later among the works it cites.