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A major goal of artificial intelligence (AI) is to create an agent capable of acquiring a general understanding of the world.
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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Routing networks and the challenges of modular and compositional computation
Rosenbaum, C., Cases, I., Riemer, M., and Klinger, T. (2019) · 1904
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Three scenarios for continual learning
van de Ven, G. M. and Tolias, A. S. (2019) · 1904
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Continual learning for robotics
Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., and Dıaz-Rodrıguez, N. (2019) · 1907
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Online continual learning from imbalanced data
Chrysakis, A. and Moens, M.-F. (2020) · 1961
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Pros and cons of the pivot and transfer approaches in multilingual machine translation
Boitet, C. (1988) · 1988
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Catastrophic interference in connectionist networks: The sequential learning problem
McCloskey, M. and Cohen, N. J. (1989) · 1989
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Feudal reinforcement learning
Dayan, P. and Hinton, G. E. (1993) · 1993
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Lifelong learning algorithms
Thrun, S. (1998) · 1998
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Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Sutton, R. S., Precup, D., and Singh, S. (1999) · 1999
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Hierarchical reinforcement learning with the MAXQ value function decomposition
Dietterich, T. G. (2000) · 2000
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Recent advances in hierarchical reinforcement learning
Barto, A. G. and Mahadevan, S. (2003) · 2003
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Evaluating logical generalization in graph neural networks
Sinha, K., Sodhani, S., Pineau, J., and Hamilton, W. L. (2020) · 2003
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Levine, S., Kumar, A., Tucker, G., and Fu, J. (2020) · 2005
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009) · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G. (2009) · 2009
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Compositionality of optimal control laws
Todorov, E. (2009) · 2009
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Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction
Sutton, R. S., Modayil, J., Delp, M., Degris, T., Pilarski, P. M., White, A., and Precup, D. (2011) · 2011
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Towards continual reinforcement learning: A review and perspectives
Khetarpal, K., Riemer, M., Rish, I., and Precup, D. (2020) · 2012
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The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M. (2013) · 2013
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ELLA: An efficient lifelong learning algorithm
Ruvolo, P. and Eaton, E. (2013) · 2013
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Online multi-task learning for policy gradient methods
Bou Ammar, H., Eaton, E., Ruvolo, P., and Taylor, M. (2014) · 2014
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PAC-inspired option discovery in lifelong reinforcement learning
Brunskill, E. and Li, L. (2014) · 2014
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Hierarchical reinforcement learning: A survey
Al-Emran, M. (2015) · 2015
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Autonomous cross-domain knowledge transfer in lifelong policy gradient reinforcement learning
Bou Ammar, H., Eaton, E., Luna, J. M., and Ruvolo, P. (2015) · 2015
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Neural module networks
Andreas, J., Rohrbach, M., Darrell, T., and Klein, D. (2016) · 2016
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Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W. (2016) · 2016
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Image style transfer using convolutional neural networks
Gatys, L. A., Ecker, A. S., and Bethge, M. (2016) · 2016
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Using task features for zero-shot knowledge transfer in lifelong learning
Isele, D., Rostami, M., and Eaton, E. (2016) · 2016
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Neural programmers-interpreters
Reed, S. and de Freitas, N. (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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Learning simple algorithms from examples
Zaremba, W., Mikolov, T., Joulin, A., and Fergus, R. (2016) · 2016
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Expert gate: Lifelong learning with a network of experts
Aljundi, R., Chakravarty, P., and Tuytelaars, T. (2017) · 2017
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The option-critic architecture
Bacon, P.-L., Harb, J., and Precup, D. (2017) · 2017
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Programming with a differentiable Forth interpreter
Bošnjak, M., Rocktäschel, T., Naradowsky, J., and Riedel, S. (2017) · 2017
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Making neural programming architectures generalize via recursion
Cai, J., Shin, R., and Song, D. (2017) · 2017
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Learning modular neural network policies for multi-task and multi-robot transfer
Devin, C., Gupta, A., Darrell, T., Abbeel, P., and Levine, S. (2017) · 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) · 2017
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Differentiable programs with neural libraries
Gaunt, A. L., Brockschmidt, M., Kushman, N., and Tarlow, D. (2017) · 2017
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Benchmark environments for multitask learning in continuous domains
Henderson, P., Chang, W.-D., Shkurti, F., Hansen, J., Meger, D., and Dudek, G. (2017) · 2017
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Learning to reason: End-to-end module networks for visual question answering
Hu, R., Andreas, J., Rohrbach, M., Darrell, T., and Saenko, K. (2017) · 2017
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Inferring and executing programs for visual reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Hoffman, J., Fei-Fei, L., Lawrence Zitnick, C., and Girshick, R. (2017) · 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., Hassabis, D., Clopath, C., Kumaran, D., and Hadsell, R. (2017) · 2017
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Learning without forgetting
Li, Z. and Hoiem, D. (2017) · 2017
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Gradient episodic memory for continual learning
Lopez-Paz, D. and Ranzato, M. (2017) · 2017
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Continual learning with deep generative replay
Shin, H., Lee, J. K., Kim, J., and Kim, J. (2017) · 2017
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A deep hierarchical approach to lifelong learning in Minecraft
Tessler, C., Givony, S., Zahavy, T., Mankowitz, D., and Mannor, S. (2017) · 2017
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Hybrid reward architecture for reinforcement learning
van Seijen, H., Fatemi, M., Romoff, J., Laroche, R., Barnes, T., and Tsang, J. (2017) · 2017
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FeUdal networks for hierarchical reinforcement learning
Vezhnevets, A. S., Osindero, S., Schaul, T., Heess, N., Jaderberg, M., Silver, D., and Kavukcuoglu, K. (2017) · 2017
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StarCraft II: A new challenge for reinforcement learning
Vinyals, O., Ewalds, T., Bartunov, S., Georgiev, P., Vezhnevets, A. S., Yeo, M., Makhzani, A., Küttler, H., Agapiou, J. P., Schrittwieser, J., Quan, J., Gaffney, S., Petersen, S., Simonyan, K., Schaul, T., van Hasselt, H., Silver, D., Lillicrap, T. P., Calderone, K., Keet, P., Brunasso, A., Lawrence, D., Ekermo, A., Repp, J., and Tsing, R. (2017) · 2017
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Continual learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S. (2017) · 2017
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Tensor based knowledge transfer across skill categories for robot control
Zhao, C., Hospedales, T. M., Stulp, F., and Sigaud, O. (2017) · 2017
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State abstractions for lifelong reinforcement learning
Abel, D., Arumugam, D., Lehnert, L., and Littman, M. (2018) · 2018
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Life-long disentangled representation learning with cross-domain latent homologies
Achille, A., Eccles, T., Matthey, L., Burgess, C., Watters, N., Lerchner, A., and Higgins, I. (2018) · 2018
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Modular meta-learning
Alet, F., Lozano-Perez, T., and Kaelbling, L. P. (2018) · 2018
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Systematic generalization: What is required and can it be learned?
Bahdanau, D., Murty, S., Noukhovitch, M., Nguyen, T. H., de Vries, H., and Courville, A. (2018) · 2018
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Transfer in deep reinforcement learning using successor features and generalised policy improvement
Barreto, A., Borsa, D., Quan, J., Schaul, T., Silver, D., Hessel, M., Mankowitz, D., Zidek, A., and Munos, R. (2018) · 2018
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Leveraging grammar and reinforcement learning for neural program synthesis
Bunel, R., Hausknecht, M., Devlin, J., Singh, R., and Kohli, P. (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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Lifelong machine learning
Chen, Z. and Liu, B. (2018) · 2018
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Composable deep reinforcement learning for robotic manipulation
Haarnoja, T., Pong, V., Zhou, A., Dalal, M., Abbeel, P., and Levine, S. (2018) · 2018
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Selective experience replay for lifelong learning
Isele, D. and Cosgun, A. (2018) · 2018
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Modular networks: Learning to decompose neural computation
Kirsch, L., Kunze, J., and Barber, D. (2018) · 2018
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Lake, B. and Baroni, M. (2018) · 2018
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Beyond shared hierarchies: Deep multitask learning through soft layer ordering
Meyerson, E. and Miikkulainen, R. (2018) · 2018
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Variational continual learning
Nguyen, C. V., Li, Y., Bui, T. D., and Turner, R. E. (2018) · 2018
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Online structured Laplace approximations for overcoming catastrophic forgetting
Ritter, H., Botev, A., and Barber, D. (2018) · 2018
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Routing networks: Adaptive selection of non-linear functions for multi-task learning
Rosenbaum, C., Klinger, T., and Riemer, M. (2018) · 2018
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Progress & compress: A scalable framework for continual learning
Schwarz, J., Czarnecki, W., Luketina, J., Grabska-Barwinska, A., Teh, Y. W., Pascanu, R., and Hadsell, R. (2018) · 2018
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Overcoming catastrophic forgetting with hard attention to the task
Serrà, J., Surís, D., Miron, M., and Karatzoglou, A. (2018) · 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) · 2018
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Houdini: Lifelong learning as program synthesis
Valkov, L., Chaudhari, D., Srivastava, A., Sutton, C., and Chaudhuri, S. (2018) · 2018
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Neural task programming: Learning to generalize across hierarchical tasks
Xu, D., Nair, S., Zhu, Y., Gao, J., Garg, A., Fei-Fei, L., and Savarese, S. (2018) · 2018
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Lifelong learning with dynamically expandable networks
Yoon, J., Lee, J., Yang, E., and Hwang, S. J. (2018) · 2018
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Task agnostic continual learning using online variational Bayes
Zeno, C., Golan, I., Hoffer, E., and Soudry, D. (2018) · 2018
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Uncertainty-based continual learning with adaptive regularization
Ahn, H., Cha, S., Lee, D., and Moon, T. (2019) · 2019
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Neural relational inference with fast modular meta-learning
Alet, F., Weng, E., Lozano-Pérez, T., and Kaelbling, L. P. (2019) · 2019
Cited alongside, same era.
Measuring compositionality in representation learning
Andreas, J. (2019) · 2019
Cited alongside, same era.
Measuring and regularizing networks in function space
Benjamin, A., Rolnick, D., and Kording, K. (2019) · 2019
Cited alongside, same era.
Automatically composing representation transformations as a means for generalization
Chang, M., Gupta, A., Levine, S., and Griffiths, T. L. (2019) · 2019
Cited alongside, same era.
BabyAI: First steps towards grounded language learning with a human in the loop
Chevalier-Boisvert, M., Bahdanau, D., Lahlou, S., Willems, L., Saharia, C., Nguyen, T. H., and Bengio, Y. (2019) · 2019
Cited alongside, same era.
CURIOUS: Intrinsically motivated modular multi-goal reinforcement learning
Brain-inspired replay for continual learning with artificial neural networks
van de Ven, G. M., Siegelmann, H. T., and Tolias, A. S. (2020) · 2020
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Continual learning with hypernetworks
von Oswald, J., Henning, C., Sacramento, J., and Grewe, B. F. (2020) · 2020
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Continual learning of control primitives : Skill discovery via reset-games
Xu, K., Verma, S., Finn, C., and Levine, S. (2020) · 2020
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Multi-task reinforcement learning with soft modularization
Yang, R., Xu, H., WU, Y., and Wang, X. (2020) · 2020
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Scalable and order-robust continual learning with additive parameter decomposition
Yoon, J., Kim, S., Yang, E., and Hwang, S. J. (2020) · 2020
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One network fits all? Modular versus monolithic task formulations in neural networks
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Colas, C., Fournier, P., Chetouani, M., Sigaud, O., and Oudeyer, P.-Y. (2019) · 2019
Cited alongside, same era.
Episodic memory in lifelong language learning
de Masson d’Autume, C., Ruder, S., Kong, L., and Yogatama, D. (2019) · 2019
Cited alongside, same era.
Compositional plan vectors
Devin, C., Geng, D., Abbeel, P., Darrell, T., and Levine, S. (2019) · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019) · 2019
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Frankle, J. and Carbin, M. (2019) · 2019
Cited alongside, same era.
A meta-MDP approach to exploration for lifelong reinforcement learning
Garcia, F. and Thomas, P. S. (2019) · 2019
Cited alongside, same era.
Recursive sketches for modular deep learning
Ghazi, B., Panigrahy, R., and Wang, J. (2019) · 2019
Cited alongside, same era.
Agarwala, A., Das, A., Juba, B., Panigrahy, R., Sharan, V., Wang, X., and Zhang, Q. (2021) · 2021
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CausalWorld: A robotic manipulation benchmark for causal structure and transfer learning
Ahmed, O., Träuble, F., Goyal, A., Neitz, A., Wuthrich, M., Bengio, Y., Schölkopf, B., and Bauer, S. (2021) · 2021
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Robust visual reasoning via language guided neural module networks
Akula, A., Jampani, V., Changpinyo, S., and Zhu, S.-C. (2021) · 2021
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Learning to recombine and resample data for compositional generalization
Akyürek, E., Akyürek, A. F., and Andreas, J. (2021) · 2021
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Compositional transformers for scene generation
Arad Hudson, D. and Zitnick, L. (2021) · 2021
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EEC: Learning to encode and regenerate images for continual learning
Ayub, A. and Wagner, A. (2021) · 2021
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Generative vs. discriminative: Rethinking the meta-continual learning
Banayeeanzade, M., Mirzaiezadeh, R., Hasani, H., and Soleymani, M. (2021) · 2021
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Composable geometric motion policies using multi-task pullback bundle dynamical systems
Bylard, A., Bonalli, R., and Pavone, M. (2021) · 2021
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CPR: Classifier-projection regularization for continual learning
Cha, S., Hsu, H., Hwang, T., Calmon, F., and Moon, T. (2021) · 2021
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Modularity in reinforcement learning via algorithmic independence in credit assignment
Chang, M., Kaushik, S., Levine, S., and Griffiths, T. (2021) · 2021
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Long live the lottery: The existence of winning tickets in lifelong learning
Chen, T., Zhang, Z., Liu, S., Chang, S., and Wang, Z. (2021) · 2021
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RMP flow
Cheng, C.-A., Mukadam, M., Issac, J., Birchfield, S., Fox, D., Boots, B., and Ratliff, N. (2021) · 2021
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Are neural nets modular? Inspecting functional modularity through differentiable weight masks
Csordás, R., van Steenkiste, S., and Schmidhuber, J. (2021) · 2021
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How modular should neural module networks be for systematic generalization?
D’Amario, V., Sasaki, T., and Boix, X. (2021) · 2021
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Flattening sharpness for dynamic gradient projection memory benefits in continual learning
Deng, D., Chen, G., Hao, J., Wang, Q., and Heng, P.-A. (2021) · 2021
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Kernel continual learning
Derakhshani, M. M., Zhen, X., Shao, L., and Snoek, C. (2021) · 2021
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BooVAE: Boosting approach for continual learning of VAE
Egorov, E., Kuzina, A., and Burnaev, E. (2021) · 2021
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Continual learning in recurrent neural networks
Ehret, B., Henning, C., Cervera, M., Meulemans, A., von Oswald, J., and Grewe, B. F. (2021) · 2021
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Recurrent independent mechanisms
Goyal, A., Lamb, A., Hoffmann, J., Sodhani, S., Levine, S., Bengio, Y., and Schölkopf, B. (2021) · 2021
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Environment generation for zero-shot compositional reinforcement learning
Gur, I., Jaques, N., Miao, Y., Choi, J., Tiwari, M., Lee, H., and Faust, A. (2021) · 2021
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Posterior meta-replay for continual learning
Henning, C., Cervera, M., D’Angelo, F., von Oswald, J., Traber, R., Ehret, B., Kobayashi, S., Grewe, B. F., and Sacramento, J. (2021) · 2021
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Optimizing reusable knowledge for continual learning via metalearning
Hurtado, J., Raymond, A., and Soto, A. (2021) · 2021
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Gradient-based editing of memory examples for online task-free continual learning
Jin, X., Sadhu, A., Du, J., and Ren, X. (2021) · 2021
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Compositional reinforcement learning from logical specifications
Jothimurugan, K., Bansal, S., Bastani, O., and Alur, R. (2021) · 2021
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Natural continual learning: Success is a journey, not (just) a destination
Kao, T.-C., Jensen, K., van de Ven, G. M., Bernacchia, A., and Hennequin, G. (2021) · 2021
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Variational auto-regressive Gaussian processes for continual learning
Kapoor, S., Karaletsos, T., and Bui, T. D. (2021) · 2021
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Achieving forgetting prevention and knowledge transfer in continual learning
Ke, Z., Liu, B., Ma, N., Xu, H., and Shu, L. (2021) · 2021
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Bayesian structural adaptation for continual learning
Kumar, A., Chatterjee, S., and Rai, P. (2021) · 2021
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Generalized variational continual learning
Loo, N., Swaroop, S., and Turner, R. E. (2021) · 2021
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Reset-free lifelong learning with skill-space planning
Lu, K., Grover, A., Abbeel, P., and Mordatch, I. (2021) · 2021
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Lifelong learning of compositional structures
Mendez, J. A. and Eaton, E. (2021) · 2021
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Linear mode connectivity in multitask and continual learning
Mirzadeh, S. I., Farajtabar, M., Gorur, D., Pascanu, R., and Ghasemzadeh, H. (2021) · 2021
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Continuous coordination as a realistic scenario for lifelong learning
Nekoei, H., Badrinaaraayanan, A., Courville, A., and Chandar, S. (2021) · 2021
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Continual learning via local module composition
Ostapenko, O., Rodriguez, P., Caccia, M., and Charlin, L. (2021) · 2021
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Hierarchical reinforcement learning: A comprehensive survey
Pateria, S., Subagdja, B., Tan, A.-h., and Quek, C. (2021) · 2021
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BNS: Building network structures dynamically for continual learning
Qin, Q., Hu, W., Peng, H., Zhao, D., and Liu, B. (2021) · 2021
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Recent advances of continual learning in computer vision: An overview
Qu, H., Rahmani, H., Xu, L., Williams, B., and Liu, J. (2021) · 2021
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Formalizing the generalization-forgetting trade-off in continual learning
Raghavan, K. and Balaprakash, P. (2021) · 2021
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Spatially structured recurrent modules
Rahaman, N., Goyal, A., Gondal, M. W., Wuthrich, M., Bauer, S., Sharma, Y., Bengio, Y., and Schölkopf, B. (2021) · 2021
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Anatomy of catastrophic forgetting: Hidden representations and task semantics
Ramasesh, V. V., Dyer, E., and Raghu, M. (2021) · 2021
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Zero-shot text-to-image generation
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I. (2021) · 2021
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Lifelong domain adaptation via consolidated internal distribution
Rostami, M. (2021) · 2021
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Independent prototype propagation for zero-shot compositionality
Ruis, F., Burghouts, G., and Bucur, D. (2021) · 2021
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Gradient projection memory for continual learning
Saha, G., Garg, I., and Roy, K. (2021) · 2021
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Class normalization for (continual)? generalized zero-shot learning
Skorokhodov, I. and Elhoseiny, M. (2021) · 2021
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Graph-based continual learning
Tang, B. and Matteson, D. S. (2021) · 2021
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CAM-GAN: Continual adaptation modules for generative adversarial networks
Varshney, S., Verma, V. K., Srijith, P. K., Carin, L., and Rai, P. (2021) · 2021
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Efficient continual learning with modular networks and task-driven priors
Veniat, T., Denoyer, L., and Ranzato, M. (2021) · 2021
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Learning where to learn: Gradient sparsity in meta and continual learning
von Oswald, J., Zhao, D., Kobayashi, S., Schug, S., Caccia, M., Zucchet, N., and Sacramento, J. (2021) · 2021
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AFEC: Active forgetting of negative transfer in continual learning
Wang, L., Zhang, M., Jia, Z., Li, Q., Bao, C., Ma, K., Zhu, J., and Zhong, Y. (2021) · 2021
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Continual World: A robotic benchmark for continual reinforcement learning
Wołczyk, M., Zając, M., Pascanu, R., Kuciński, Ł., and Miłoś, P. (2021) · 2021
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Improving compositionality of neural networks by decoding representations to inputs
Wu, M., Goodman, N., and Ermon, S. (2021) · 2021
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Mitigating forgetting in online continual learning with neuron calibration
Yin, H., Yang, P., and Li, P. (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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Task-agnostic continual learning using online variational Bayes with fixed-point updates
Zeno, C., Golan, I., Hoffer, E., and Soudry, D. (2021) · 2021
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Variational continual Bayesian meta-learning
Zhang, Q., Fang, J., Meng, Z., Liang, S., and Yilmaz, E. (2021) · 2021
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Constructing a good behavior basis for transfer using generalized policy updates
Alver, S. and Precup, D. (2022) · 2022
Closest in time.
CoMPS: Continual meta policy search
Berseth, G., Zhang, Z., Zhang, G., Finn, C., and Levine, S. (2022) · 2022
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NEVIS’22: A stream of 100 tasks sampled from 30 years of computer vision research
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