plasticity
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SAFE TrainedModels
After calibrating in the first session, the slow efficient tuning parameters can capture more informativefeatures, improving generalization to incoming classes. Moreover, to further incorporate novel concepts, we strikeabalance between stability and plasticity byfixing slowefficient tuning parameters and continuously updating the fast ones. Specifically, a cross-classification loss with feature alignment is proposed to circumvent catastrophic forgetting.
COOM: A Game Benchmark for Continual Reinforcement Learning Tristan Tomilin 1 Meng Fang
RL benchmark tailored for embodied pixel-based RL.
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- Europe > Bulgaria (0.04)
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Random Path Selection for Continual Learning
Jathushan Rajasegaran, Munawar Hayat, Salman H. Khan, Fahad Shahbaz Khan, Ling Shao
Neural Information Processing Systems http://nips.cc/
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Dendritic cortical microcircuits approximate the backpropagation algorithm
João Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn
Neural Information Processing Systems http://nips.cc/
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Gradient Descent for Spiking Neural Networks
Dongsung Huh, Terrence J. Sejnowski
Neural Information Processing Systems http://nips.cc/
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