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In an ever expanding set of research and application areas, deep neural networks (DNNs) set the bar for algorithm performance.
Improved Knowledge Distillation via Teacher Assistant
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Neuronlike adaptive elements that can solve difficult learning control problems
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Efficient memory-based learning for robot control
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Survey and critique of techniques for extracting rules from trained artificial neural networks
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Learning to use selective attention and short-term memory in sequential tasks
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Model compression
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Logistic model tree extraction from artificial neural networks
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Scikit-learn: Machine Learning in Python
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A reduction of imitation learning and structured prediction to no-regret online learning
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Playing atari with deep reinforcement learning
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Graves, A.; Antonoglou, I.; Wierstra, D.; and Riedmiller, M. 2013 · 2013
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Do deep nets really need to be deep?
Ba, J.; and Caruana, R. 2014 · 2014
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Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
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Rusu, A. A.; Colmenarejo, S. G.; Gulcehre, C.; Desjardins, G.; Kirkpatrick, J.; Pascanu, R.; Mnih, V.; Kavukcuoglu, K.; and Hadsell, R. 2015 · 2015
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Interpretability via model extraction
Bastani, O.; Kim, C.; and Bastani, H. 2017 · 2017
Verifiable reinforcement learning via policy extraction
Bastani, O.; Pu, Y.; and Solar-Lezama, A. 2018 · 2018
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Toward interpretable deep reinforcement learning with linear model u-trees
Liu, G.; Schulte, O.; Zhu, W.; and Li, Q. 2018 · 2018
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Model compression via distillation and quantization
Polino, A.; Pascanu, R.; and Alistarh, D. 2018 · 2018
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Kickstarting deep reinforcement learning
Schmitt, S.; Hudson, J. J.; Zidek, A.; Osindero, S.; Doersch, C.; Czarnecki, W. M.; Leibo, J. Z.; Kuttler, H.; Zisserman, A.; Simonyan, K.; et al. 2018 · 2018
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Distilling deep reinforcement learning policies in soft decision trees
Coppens, Y.; Efthymiadis, K.; Lenaerts, T.; and Nowe, A. 2019 · 2019
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Learning efficient object detection models with knowledge distillation
Chen, G.; Choi, W.; Yu, X.; Han, T.; and Chandraker, M. 2017 · 2017
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OpenAI Baselines
Dhariwal, P.; Hesse, C.; Klimov, O.; Nichol, A.; Plappert, M.; Radford, A.; Schulman, J.; Sidor, S.; Wu, Y.; and Zhokhov, P. 2017 · 2017
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Distilling a neural network into a soft decision tree
Frosst, N.; and Hinton, G. 2017 · 2017
Cited alongside, same era.
TED: Teaching AI to Explain its Decisions
Hind, M.; Wei, D.; Campbell, M.; Codella, N. C. F.; Dhurandhar, A.; Mojsilovic, A.; Ramamurthy, K. N.; and Varshney, K. R. 2019 · 2019
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Learning from Data: Kernel Methods
Abu-Mostafa, Y. 2020 · 2020
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Distill-NN-Tree
Martak, L. 2020 · 2020
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