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There has been a surge of interest in continual learning and federated learning, both of which are important in deep neural networks in real-world scenarios.
A Lifelong Learning Perspective for Mobile Robot Control
Thrun, S · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G. E · 2009
Earlier work this paper cites.
Not-mnist dataset
Bulatov, Y · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
Earlier work this paper cites.
The german traffic sign recognition benchmark: a multi-class classification competition
Stallkamp, J., Schlipsing, M., Salmen, J., and Igel, C · 2011
Earlier work this paper cites.
Learning task grouping and overlap in multi-task learning
Kumar, A. and Daume III, H · 2012
Earlier work this paper cites.
Ella: An efficient lifelong learning algorithm
Ruvolo, P. and Eaton, E · 2013
Earlier work this paper cites.
A data-driven approach to cleaning large face datasets
Ng, H.-W. and Winkler, S · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., et al · 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., et al · 2017
Earlier work this paper cites.
Overcoming catastrophic forgetting by incremental moment matching
Lee, S.-W., Kim, J.-H., Jun, J., Ha, J.-W., and Zhang, B.-T · 2017
Earlier work this paper cites.
Gradient episodic memory for continual learning
Lopez-Paz, D. and Ranzato, M · 2017
Earlier work this paper cites.
Continual learning with deep generative replay
Shin, H., Lee, J. K., Kim, J., and Kim, J · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
Cited alongside, same era.
Federated optimization in heterogeneous networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2018
Cited alongside, same era.
Variational continual learning
Nguyen, C. V., Li, Y., Bui, T. D., and Turner, R. E · 2018
Cited alongside, same era.
Multi-agent distributed lifelong learning for collective knowledge acquisition
Rostami, M., Kolouri, S., Kim, K., and Eaton, E · 2018
Cited alongside, same era.
Learning to learn without forgetting by maximizing transfer and minimizing interference
Riemer, M., Cases, I., Ajemian, R., Liu, M., Rish, I., Tu, Y., and Tesauro, G · 2019
Later among the works it cites.
Overcoming forgetting in federated learning on non-iid data
Shoham, N., Avidor, T., Keren, A., Israel, N., Benditkis, D., Mor-Yosef, L., and Zeitak, I · 2019
Later among the works it cites.
Bayesian nonparametric federated learning of neural networks
Yurochkin, M., Agarwal, M., Ghosh, S., Greenewald, K., Hoang, T. N., and Khazaeni, Y · 2019
Later among the works it cites.
Continual learning in low-rank orthogonal subspaces
Chaudhry, A., Khan, N., Dokania, P. K., and Torr, P. H · 2020
Closest in time.
Adaptive personalized federated learning
Deng, Y., Kamani, M. M., and Mahdavi, M · 2020
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Schwarz, J., Luketina, J., Czarnecki, W. M., Grabska-Barwinska, A., Teh, Y. W., Pascanu, R., and Hadsell, R · 2018
Cited alongside, same era.
Overcoming catastrophic forgetting with hard attention to the task
Serrà, J., Surís, D., Miron, M., and Karatzoglou, A · 2018
Cited alongside, same era.
Reinforced continual learning
Xu, J. and Zhu, Z · 2018
Cited alongside, same era.
Lifelong learning with dynamically expandable networks
Yoon, J., Yang, E., Lee, J., and Hwang, S. J · 2018
Cited alongside, same era.
Efficient lifelong learning with a-gem
Chaudhry, A., Ranzato, M., Rohrbach, M., and Elhoseiny, M · 2019
Cited alongside, same era.
Chen, Y., Sun, X., and Jin, Y · 2019
Cited alongside, same era.
Compacting, picking and growing for unforgetting continual learning
Hung, C.-Y., Tu, C.-H., Wu, C.-E., Chen, C.-H., Chan, Y.-M., and Chen, C.-S · 2019
Cited alongside, same era.
Closest in time.
Personalized federated learning: A meta-learning approach
Fallah, A., Mokhtari, A., and Ozdaglar, A · 2020
Closest in time.
Scaffold: Stochastic controlled averaging for on-device federated learning
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., and Suresh, A. T · 2020
Closest in time.
Unsupervised model personalization while preserving privacy and scalability: An open problem
Lange, M. D., Jia, X., Parisot, S., Leonardis, A., Slabaugh, G., and Tuytelaars, T · 2020
Closest in time.
Understanding the role of training regimes in continual learning
Mirzadeh, S. I., Farajtabar, M., Pascanu, R., and Ghasemzadeh, H · 2020
Closest in time.
Functional regularisation for continual learning with gaussian processes
Titsias, M. K., Schwarz, J., Matthews, A. G. d. G., Pascanu, R., and Teh, Y. W · 2020
Closest in time.
Federated learning with matched averaging
Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., and Khazaeni, Y · 2020
Closest in time.
Scalable and order-robust continual learning with additive parameter decomposition
Yoon, J., Kim, S., Yang, E., and Hwang, S. J · 2020
Closest in time.
Linear mode connectivity in multitask and continual learning
Mirzadeh, S. I., Farajtabar, M., Gorur, D., Pascanu, R., and Ghasemzadeh, H · 2021
Closest in time.