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This paper argues that continual learning methods can benefit by splitting the capacity of the learner across multiple models.
On tiny episodic memories in continual learning
Chaudhry, A., Rohrbach, M., Elhoseiny, M., Ajanthan, T., Dokania, P. K., Torr, P. H., and Ranzato, M. (2019b) · 1902
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A unifying bayesian view of continual learning
Farquhar, S. and Gal, Y. (2019b) · 1902
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Scalable and order-robust continual learning with additive parameter decomposition
Yoon, J., Kim, S., Yang, E., and Hwang, S. J. (2019) · 1902
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Three scenarios for continual learning
Van de Ven, G. M. and Tolias, A. S. (2019) · 1904
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Branched multi-task networks: Deciding what layers to share
Vandenhende, S., Georgoulis, S., De Brabandere, B., and Van Gool, L. (2019) · 1904
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Adashare: Learning what to share for efficient deep multi-task learning
Sun, X., Panda, R., Feris, R., and Saenko, K. (2019) · 1911
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Catastrophic forgetting, rehearsal and pseudorehearsal
Robins, A. (1995) · 1995
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Arcing classifier (with discussion and a rejoinder by the author)
Breiman, L. (1998) · 1998
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Statistical Learning Theory
Vapnik, V. (1998) · 1998
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Exploiting generative models in discriminative classifiers
Jaakkola, T. and Haussler, D. (1999) · 1999
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Boosting algorithms as gradient descent
Mason, L., Baxter, J., Bartlett, P., and Frean, M. (1999) · 1999
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A model of inductive bias learning
Baxter, J. (2000) · 2000
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Few-Shot Learning via Learning the Representation, Provably
Du, S. S., Hu, W., Kakade, S. M., Lee, J. D., and Lei, Q. (2020) · 2002
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Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Wen, Y., Tran, D., and Ba, J. (2020) · 2002
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Exploiting task relatedness for learning multiple tasks
Ben-David, S. and Schuller, R. (2003) · 2003
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Omnidirectional transfer for quasilinear lifelong learning
Vogelstein, J. T., Dey, J., Helm, H. S., LeVine, W., Mehta, R. D., Geisa, A., van de Ven, G. M., Chang, E., Gao, C., Yang, W., et al. (2020) · 2004
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Learning multiple tasks with kernel methods
Evgeniou, T., Micchelli, C. A., Pontil, M., and Shawe-Taylor, J. (2005) · 2005
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A no-free-lunch theorem for multitask learning
Hanneke, S. and Kpotufe, S. (2020) · 2006
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Bounds for linear multi-task learning
Maurer, A. (2006) · 2006
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Understanding the role of training regimes in continual learning
Mirzadeh, S. I., Farajtabar, M., Pascanu, R., and Ghasemzadeh, H. (2020b) · 2006
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On the Theory of Transfer Learning: The Importance of Task Diversity
Tripuraneni, N., Jordan, M. I., and Jin, C. (2020) · 2006
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A notion of task relatedness yielding provable multiple-task learning guarantees
Ben-David, S. and Borbely, R. S. (2008) · 2008
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Learning from multiple sources
Crammer, K., Kearns, M., and Wortman, J. (2008) · 2008
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A theory of learning from different domains
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Vaughan, J. W. (2010) · 2010
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Linear algorithms for online multitask classification
Cavallanti, G., Cesa-Bianchi, N., and Gentile, C. (2010) · 2010
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Linear mode connectivity in multitask and continual learning
Mirzadeh, S. I., Farajtabar, M., Gorur, D., Pascanu, R., and Ghasemzadeh, H. (2020a) · 2010
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iCARL: Incremental classifier and representation learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., and Lampert, C. H. (2017b) · 2010
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Learning task grouping and overlap in multi-task learning
Kumar, A. and Daume III, H. (2012) · 2012
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Memory aware synapses: Learning what (not) to forget
Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., and Tuytelaars, T. (2018) · 2018
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
Chaudhry, A., Dokania, P. K., Ajanthan, T., and Torr, P. H. (2018) · 2018
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Don’t forget, there is more than forgetting: new metrics for continual learning
Díaz-Rodríguez, N., Lomonaco, V., Filliat, D., and Maltoni, D. (2018) · 2018
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A system for massively parallel hyperparameter tuning
Li, L., Jamieson, K., Rostamizadeh, A., Gonina, E., Hardt, M., Recht, B., and Talwalkar, A. (2018) · 2018
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Tune: A research platform for distributed model selection and training
Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J. E., and Stoica, I. (2018) · 2018
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Thrun, S. and Pratt, L. (2012) · 2012
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Boosting: Foundations and Algorithms
Schapire, R. E. and Freund, Y. (2013) · 2013
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Lifelong learning with non-iid tasks
Pentina, A. and Lampert, C. H. (2015) · 2015
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Cross-stitch networks for multi-task learning
Misra, I., Shrivastava, A., Gupta, A., and Hebert, M. (2016) · 2016
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Online contrastive divergence with generative replay: Experience replay without storing data
Mocanu, D. C., Vega, M. T., Eaton, E., Stone, P., and Liotta, A. (2016) · 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) · 2016
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Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al. (2016) · 2016
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Piggyback: Adapting a single network to multiple tasks by learning to mask weights
Mallya, A., Davis, D., and Lazebnik, S. (2018) · 2018
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Packnet: Adding multiple tasks to a single network by iterative pruning
Mallya, A. and Lazebnik, S. (2018) · 2018
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Nevergrad - A gradient-free optimization platform
Rapin, J. and Teytaud, O. (2018) · 2018
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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. (2018) · 2018
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Overcoming catastrophic forgetting with hard attention to the task
Serra, J., Suris, D., Miron, M., and Karatzoglou, A. (2018) · 2018
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Reinforced continual learning
Xu, J. and Zhu, Z. (2018) · 2018
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On the value of target data in transfer learning
Hanneke, S. and Kpotufe, S. (2019) · 2019
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Recurrence is required to capture the representational dynamics of the human visual system
Kietzmann, T. C., Spoerer, C. J., Sörensen, L. K., Cichy, R. M., Hauk, O., and Kriegeskorte, N. (2019) · 2019
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Orthogonal gradient descent for continual learning
Farajtabar, M., Azizan, N., Mott, A., and Li, A. (2020) · 2020
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Generative feature replay for class-incremental learning
Liu, X., Wu, C., Menta, M., Herranz, L., Raducanu, B., Bagdanov, A. D., Jui, S., and de Weijer, J. v. (2020) · 2020
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Continual deep learning by functional regularisation of memorable past
Pan, P., Swaroop, S., Immer, A., Eschenhagen, R., Turner, R., and Khan, M. E. E. (2020) · 2020
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Gdumb: A simple approach that questions our progress in continual learning
Prabhu, A., Torr, P. H., and Dokania, P. K. (2020) · 2020
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Functional regularisation for continual learning with gaussian processes
Titsias, M. K., Schwarz, J., de G. Matthews, A. G., Pascanu, R., and Teh, Y. W. (2020) · 2020
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Brain-inspired replay for continual learning with artificial neural networks
Ven, G. M., Siegelmann, H. T., Tolias, A. S., et al. (2020) · 2020
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Kaushik, P., Gain, A., Kortylewski, A., and Yuille, A. (2021) · 2021
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Tag: Task-based accumulated gradients for lifelong learning
Malviya, P., Ravindran, B., and Chandar, S. (2021) · 2021
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Encoders and ensembles for task-free continual learning
Shanahan, M., Kaplanis, C., and Mitrović, J. (2021) · 2021
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Federated continual learning with weighted inter-client transfer
Yoon, J., Jeong, W., Lee, G., Yang, E., and Hwang, S. J. (2021) · 2021
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