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We introduce Continual Learning via Neural Pruning (CLNP), a new method aimed at lifelong learning in fixed capacity models based on neuronal model sparsification.
Superposition of many models into one
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The State of Sparsity in Deep Neural Networks
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How transferable are features in deep neural networks?
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Sparsifying Neural Network Connections for Face Recognition
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Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell. 2016 · 2016
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Progressive Neural Networks
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Lifelong Learning with Dynamically Expandable Networks
Jeongtae Lee, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang. 2017 · 2017
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ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin. 2017 · 2017
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Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
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Continual learning with intelligent synapses
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Power Law in Sparsified Deep Neural Networks
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C. Fernando, D. Banarse, C. Blundell, Y. Zwols, D. Ha, A. A. Rusu, A. Pritzel, and D. Wierstra. 2017 · 2017
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