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Program execution speed critically depends on increasing cache hits, as cache hits are orders of magnitude faster than misses.
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A reduction of imitation learning and structured prediction to no-regret online learning
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Ship: Signature-based hit predictor for high performance caching
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Ross, S. and Bagnell, J. A · 2014
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Characterizing Facebook’s memcached workload
Xu, Y., Frachtenberg, E., Jiang, S., and Paleczny, M · 2014
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Scheduled sampling for sequence prediction with recurrent neural networks
Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N · 2015
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Han, S., Mao, H., and Dally, W. J · 2015
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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Effective approaches to attention-based neural machine translation
Luong, M.-T., Pham, H., and Manning, C. D · 2015
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Cliffhanger: Scaling performance cliffs in web memory caches
Cidon, A., Eisenman, A., Alizadeh, M., and Katti, S · 2016
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Efficient processing of deep neural networks: A tutorial and survey
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Attention is all you need
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Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
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Learning memory access patterns
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Deep Q-learning from demonstrations
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Optimal completion distillation for sequence learning
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PyTorch: An imperative style, high-performance deep learning library
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Applying deep learning to the cache replacement problem
Shi, Z., Huang, X., Jain, A., and Lin, C · 2019
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Learning caching policies with subsampling
Wang, H., He, H., Alizadeh, M., and Mao, H · 2019
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Learning execution through neural code fusion
Shi, Z., Swersky, K., Tarlow, D., Ranganathan, P., and Hashemi, M · 2020
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