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Artificial neural networks have exceeded human-level performance in accomplishing several individual tasks (e.g.
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Semi-distributed representations and catastrophic forgetting in connectionist networks
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A simple weight decay can improve generalization
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Alcove: an exemplar-based connectionist model of category learning
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Reinforcement learning for robots using neural networks
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Prioritized sweeping: Reinforcement learning with less data and less time
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Jumpnet: A multiple-memory connectionist architecture
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Dynamically constraining connectionist networks to produce distributed, orthogonal representations to reduce catastrophic interference
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Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
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Catastrophic forgetting, rehearsal and pseudorehearsal
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Avoiding catastrophic forgetting by coupling two reverberating neural networks
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Pseudo-recurrent connectionist networks: An approach to the ’sensitivity-stability’ dilemma
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Online learning and stochastic approximations
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Multitask learning
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Clustering learning tasks and the selective cross-task transfer of knowledge
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Thrun, S., and Pratt, L · 1998
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Catastrophic forgetting in connectionist networks
French, R. M · 1999
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Modeling time perception in rats: Evidence for catastrophic interference in animal learning
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The organization of behavior. 1949
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The task rehearsal method of life-long learning: Overcoming impoverished data
Silver, D. L., and Mercer, R. E · 2002
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The cost of cortical computation
Lennie, P · 2003
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Sparse coding of sensory inputs
A Olshausen, B., and Field, D · 2004
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Incremental clustering and dynamic information retrieval
Charikar, M., Chekuri, C., Feder, T., and Motwani, R · 2004
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Online learning with kernels
Kivinen, J., Smola, A. J., and Williamson, R. C · 2004
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A heuristic algorithm to incremental support vector machine learning
Li, Z.-W., Zhang, J.-P., and Yang, J · 2004
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Reducing the dimensionality of data with neural networks
Hinton, G. E., and Salakhutdinov, R. R · 2006
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Measuring solid angles beyond dimension three
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A unified energy-based framework for unsupervised learning
Marc’Aurelio Ranzato, Y., and LeCun, L. B. S. C. Y · 2007
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Multi-task learning for classification with dirichlet process priors
Xue, Y., Liao, X., Carin, L., and Krishnapuram, B · 2007
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Adaptive online gradient descent
Hazan, E., Rakhlin, A., and Bartlett, P. L · 2008
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Automated flower classification over a large number of classes
Nilsback, M.-E., and Zisserman, A · 2008
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Online linear regression and its application to model-based reinforcement learning
Strehl, A. L., and Littman, M. L · 2008
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Positivity theorems for solid-angle polynomials
Beck, M., Robins, S., and Sam, S. V · 2009
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Learning deep architectures for ai
Bengio, Y., et al · 2009
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Character recognition in natural images
de Campos, T. E., Babu, B. R., and Varma, M · 2009
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Clustered multi-task learning: A convex formulation
Jacob, L., Vert, J.-p., and Bach, F. R · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., and Hinton, G · 2009
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Hierarchical modularity in human brain functional networks
Meunier, D., Lambiotte, R., Fornito, A., Ersche, K., and Bullmore, E · 2009
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Distribution matching for transduction
Quadrianto, N., Petterson, J., and Smola, A. J · 2009
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Recognizing indoor scenes
Quattoni, A., and Torralba, A · 2009
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Large-scale machine learning with stochastic gradient descent
Bottou, L · 2010
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icarl: Incremental classifier and representation learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., and Lampert, C. H · 2010
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Caltech-UCSD Birds 200
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Adaptive subgradient methods for online learning and stochastic optimization
Duchi, J., Hazan, E., and Singer, Y · 2011
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Deep sparse rectifier neural networks
Glorot, X., Bordes, A., and Bengio, Y · 2011
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Online regression with varying gaussians and non-identical distributions
Hu, T · 2011
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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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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Sparsifying neural network connections for face recognition
Sun, Y., Wang, X., and Tang, X · 2016
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Regularizing deep convolutional neural networks with a structured decorrelation constraint
Xiong, W., Du, B., Zhang, L., Hu, R., and Tao, D · 2016
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Net-trim: Convex pruning of deep neural networks with performance guarantee
Aghasi, A., Abdi, A., Nguyen, N., and Romberg, J · 2017
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Domain adaptation in computer vision applications
Csurka, G · 2017
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Sherlock: Scalable fact learning in images
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Learning task grouping and overlap in multi-task learning
Kumar, A., and Daumé III, H · 2012
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A lateral inhibitory spiking neural network for sparse representation in visual cortex
Liu, J., and Jia, Y · 2012
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Online learning and online convex optimization
Shalev-Shwartz, S., et al · 2012
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Adadelta: an adaptive learning rate method
Zeiler, M. D · 2012
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Vision meets robotics: The kitti dataset
Geiger, A., Lenz, P., Stiller, C., and Urtasun, R · 2013
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Goodfellow, I. J., Mirza, M., Xiao, D., Courville, A., and Bengio, Y · 2013
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Elhoseiny, M., Cohen, S., Chang, W., Price, B. L., and Elgammal, A. M · 2017
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Pathnet: Evolution channels gradient descent in super neural networks
Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A. A., Pritzel, A., and Wierstra, D · 2017
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Rainbow: Combining improvements in deep reinforcement learning
Hessel, M., Modayil, J., van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., and Silver, D · 2017
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Switching between internal and external modes: a multiscale learning principle
Honey, C. J., Newman, E. L., and Schapiro, A. C · 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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Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory
Kokkinos, I · 2017
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Overcoming catastrophic forgetting by incremental moment matching
Lee, S.-W., Kim, J.-H., Jun, J., Ha, J.-W., and Zhang, B.-T · 2017
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Gradient episodic memory for continual learning
Lopez-Paz, D., et al · 2017
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Bayesian compression for deep learning
Louizos, C., Ullrich, K., and Welling, M · 2017
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Memory based online learning of deep representations from video streams
Pernici, F., Bartoli, F., Bruni, M., and Bimbo, A. D · 2017
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Unsupervised incremental learning of deep descriptors from video streams
Pernici, F., and Del Bimbo, A · 2017
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Ramapuram, J., Gregorova, M., and Kalousis, A · 2017
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Encoder based lifelong learning
Rannen, A., Aljundi, R., Blaschko, M. B., and Tuytelaars, T · 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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Incremental learning of object detectors without catastrophic forgetting
Shmelkov, K., Schmid, C., and Alahari, K · 2017
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Improved multitask learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S · 2017
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A survey on multi-task learning
Zhang, Y., and Yang, Q · 2017
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Memory aware synapses: Learning what (not) to forget
Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., and Tuytelaars, T · 2018
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Pseudo-recursal: Solving the catastrophic forgetting problem in deep neural networks
Atkinson, C., McCane, B., Szymanski, L., and Robins, A. V · 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
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Lifelong machine learning
Chen, Z., and Liu, B · 2018
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Exploring the challenges towards lifelong fact learning
Elhoseiny, M., babiloni, F., Aljundi, R., Rohrbach, M., and Tuytelaars, T · 2018
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Towards robust evaluations of continual learning
Farquhar, S., and Gal, Y · 2018
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Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Hsu, Y.-C., Liu, Y.-C., and Kira, Z · 2018
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Note on the quadratic penalties in elastic weight consolidation
Huszár, F · 2018
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Selective experience replay for lifelong learning
Isele, D., and Cosgun, A · 2018
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Continual classification learning using generative models, 2018
Lavda, F., Ramapuram, J., Gregorova, M., and Kalousis, A · 2018
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Rotate your networks: Better weight consolidation and less catastrophic forgetting
Liu, X., Masana, M., Herranz, L., Van de Weijer, J., Lopez, A. M., and Bagdanov, A. D · 2018
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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
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Packnet: Adding multiple tasks to a single network by iterative pruning
Mallya, A., and Lazebnik, S · 2018
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Variational continual learning
Nguyen, C. V., Li, Y., Bui, T. D., and Turner, R. E · 2018
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Online deep learning: growing rbm on the fly
Ramasamy, S., Rajaraman, K., Krishnaswamy, P., and Chandrasekhar, V · 2018
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Experience replay for continual learning
Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T. P., and Wayne, G · 2018
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Unsupervised experience replay for continual learning
Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T. P., and Wayne, G · 2018
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Incremental learning through deep adaptation
Rosenfeld, A., and Tsotsos, J. K · 2018
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Online deep learning: learning deep neural networks on the fly
Sahoo, D., Pham, Q., Lu, J., and Hoi, S. C · 2018
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Overcoming catastrophic forgetting with hard attention to the task
Serrà, J., Suris, D., Miron, M., and Karatzoglou, A · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al · 2018
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Memory-based parameter adaptation
Sprechmann, P., Jayakumar, S., Rae, J., Pritzel, A., Badia, A. P., Uria, B., Vinyals, O., Hassabis, D., Pascanu, R., and Blundell, C · 2018
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Learning to accept new classes without training
Xu, H., Liu, B., Shu, L., and Yu, P. S · 2018
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Reinforced continual learning
Xu, J., and Zhu, Z · 2018
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Task-free continual learning
Aljundi, R., Kelchtermans, K., and Tuytelaars, T · 2019
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Online continual learning with no task boundaries
Aljundi, R., Lin, M., Goujaud, B., and Bengio, Y · 2019
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Selfless sequential learning
Aljundi, R., Rohrbach, M., and Tuytelaars, T · 2019
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Efficient lifelong learning with a-GEM
Chaudhry, A., Ranzato, M., Rohrbach, M., and Elhoseiny, M · 2019
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