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One weakness of machine learning algorithms is the poor ability of models to solve new problems without forgetting previously acquired knowledge.
Mixtures of dirichlet processes with applications to bayesian nonparametric problems
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Pathnet: Evolution channels gradient descent in super neural networks
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Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
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An information-theoretic perspective on credit assignment in reinforcement learning
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Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, and Marcus Rohrbach · 2020
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Meta-consolidation for continual learning
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Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, and Sergey Levine · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Heinke Hihn, Sebastian Gottwald, and Daniel A Braun · 2018
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Promp: Proximal meta-policy search
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Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, and Roger Grosse · 2018
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Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2018
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Uncertainty-based continual learning with adaptive regularization
Hongjoon Ahn, Sungmin Cha, Donggyu Lee, and Taesup Moon · 2019
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Joseph KJ and Vineeth N Balasubramanian · 2020
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A neural dirichlet process mixture model for task-free continual learning
Soochan Lee, Junsoo Ha, Dongsu Zhang, and Gunhee Kim · 2020
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Learning latent space energy-based prior model
Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, and Ying Nian Wu · 2020
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Effective diversity in population based reinforcement learning
Jack Parker-Holder, Aldo Pacchiano, Krzysztof M Choromanski, and Stephen J Roberts · 2020
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Brain-inspired replay for continual learning with artificial neural networks
Gido M van de Ven, Hava T Siegelmann, and Andreas S Tolias · 2020
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When does diversity help generalization in classification ensembles
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Using hindsight to anchor past knowledge in continual learning
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