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Continual learning - learning new tasks in sequence while maintaining performance on old tasks - remains particularly challenging for artificial neural networks.
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When and where do we apply what we learn?: A taxonomy for far transfer
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Brain imaging of language plasticity in adopted adults: Can a second language replace the first?
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On-line learning in neural networks , volume 17
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Online incremental feature learning with denoising autoencoders
Zhou, G., Sohn, K., and Lee, H · 2012
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An empirical investigation of catastrophic forgeting in gradient-based neural networks
Goodfellow, I. J., Mirza, M., Da, X., Courville, A. C., and Bengio, Y · 2014
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Sleep promotes branch-specific formation of dendritic spines after learning
Yang, G., Lai, C. S. W., Cichon, J., Ma, L., Li, W., and Gan, W.-B · 2014
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Branch-specific dendritic ca 2+ spikes cause persistent synaptic plasticity
Cichon, J. and Gan, W.-B · 2015
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A bio-inspired incremental learning architecture for applied perceptual problems
Gepperth, A. and Karaoguz, C · 2016
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What learning systems do intelligent agents need? complementary learning systems theory updated
Kumaran, D., Hassabis, D., and McClelland, J. L · 2016
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Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R · 2016
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
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Machine learning in medicine
Rajkomar, A., Dean, J., and Kohane, I · 2019
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Data-dependence of plateau phenomenon in learning with neural network — statistical mechanical analysis
Yoshida, Y. and Okada, M · 2019
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Modeling the influence of data structure on learning in neural networks: The hidden manifold model
Goldt, S., Mézard, M., Krzakala, F., and Zdeborová, L · 2020
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Bad global minima exist and sgd can reach them
Liu, S., Papailiopoulos, D., and Achlioptas, D · 2020
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Statistical mechanical analysis of catastrophic forgetting in continual learning with teacher and student networks
Asanuma, H., Takagi, S., Nagano, Y., Yoshida, Y., Igarashi, Y., and Okada, M · 2021
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Behavioral experiments for understanding catastrophic forgetting
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Neurogenesis deep learning: Extending deep networks to accommodate new classes
Draelos, T. J., Miner, N. E., Lamb, C. C., Cox, J. A., Vineyard, C. M., Carlson, K. D., Severa, W. M., James, C. D., and Aimone, J. B · 2017
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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
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Learning without forgetting
Li, Z. and Hoiem, D · 2017
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icarl: Incremental classifier and representation learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., and Lampert, C. H · 2017
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Continual learning with deep generative replay
Shin, H., Lee, J. K., Kim, J., and Kim, J · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Continual learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S · 2017
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Bell, S. J. and Lawrence, N. D · 2021
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Phase transitions in transfer learning for high-dimensional perceptrons
Dhifallah, O. and Lu, Y. M · 2021
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A theoretical analysis of catastrophic forgetting through the ntk overlap matrix
Doan, T., Bennani, M. A., Mazoure, B., Rabusseau, G., and Alquier, P · 2021
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Continual learning in the teacher-student setup: Impact of task similarity
Lee, S., Goldt, S., and Saxe, A · 2021
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Lifelong learning of compositional structures
Mendez, J. A. and EATON, E · 2021
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Wide neural networks forget less catastrophically
Mirzadeh, S. I., Chaudhry, A., Hu, H., Pascanu, R., Gorur, D., and Farajtabar, M · 2021
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Continual learning via local module composition
Ostapenko, O., Rodriguez, P., Caccia, M., and Charlin, L · 2021
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Anatomy of catastrophic forgetting: Hidden representations and task semantics
Ramasesh, V. V., Dyer, E., and Raghu, M · 2021
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Classifying high-dimensional gaussian mixtures: Where kernel methods fail and neural networks succeed
Refinetti, M., Goldt, S., Krzakala, F., and Zdeborova, L · 2021
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An analytical theory of curriculum learning in teacher-student networks
Saglietti, L., Mannelli, S. S., and Saxe, A · 2021
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Algorithmic insights on continual learning from fruit flies
Shen, Y., Dasgupta, S., and Navlakha, S · 2021
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Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., Velankar, S., Kleywegt, G. J., Bateman, A., Evans, R., Pritzel, A., Figurnov, M., Ronneberger, O., Bates, R., Kohl, S. A. A., Potapenko, A., Ballard, A. J., Romera-Paredes, B., Nikolov, S., Jain, R., Clancy, E., Reiman, D., Petersen, S., Senior, A. W., Kavukcuoglu, K., Birney, E., Kohli, P., Jumper, J., and Hassabis, D · 2021
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Efficient continual learning with modular networks and task-driven priors
Veniat, T., Denoyer, L., and Ranzato, M · 2021
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Probing transfer learning with a model of synthetic correlated datasets
Gerace, F., Saglietti, L., Sarao Mannelli, S., Saxe, A., and Zdeborová, L · 2022
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