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We propose a static loop vectorization optimization on top of high level dataflow IR used by frameworks like TensorFlow.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Learning to forget: Continual prediction with lstm
Gers, F. A., Schmidhuber, J. A., and Cummins, F. A · 2000
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Auto-vectorization of interleaved data for simd
Nuzman, D., Rosen, I., and Zaks, A · 2006
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Broadcasting
The SciPy community · 2008
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Polyhedral-model guided loop-nest auto-vectorization
Trifunovic, K., Nuzman, D., Cohen, A., Zaks, A., and Rosen, I · 2009
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Efficient selection of vector instructions using dynamic programming
Barik, R., Zhao, J., and Sarkar, V · 2010
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Whole-function vectorization
Karrenberg, R. and Hack, S · 2011
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ispc: A spmd compiler for high-performance cpu programming
Pharr, M. and Mark, W. R · 2012
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Halide: A language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines
Ragan-Kelley, J., Barnes, C., Adams, A., Paris, S., Durand, F., and Amarasinghe, S · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Variance Reduction in SGD by Distributed Importance Sampling
Alain, G., Lamb, A., Sankar, C., Courville, A., and Bengio, Y · 2015
Cited alongside, same era.
Efficient Per-Example Gradient Computations
Goodfellow, I · 2015
Cited alongside, same era.
Improved semantic representations from tree-structured long short-term memory networks
Tai, K. S., Socher, R., and Manning, C. D · 2015
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I. J., Harp, A., Irving, G., Isard, M., Jia, Y., Józefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D. G., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P. A., Vanhoucke, V., Vasudevan, V., Viégas, F. B., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2016
Cited alongside, same era.
XLA - TensorFlow, compiled
The XLA team · 2017
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DLVM: A modern compiler infrastructure for deep learning
Wei, R., Adve, V., and Schwartz, L · 2017
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TVM: end-to-end optimization stack for deep learning
Chen, T., Moreau, T., Jiang, Z., Shen, H., Yan, E. Q., Wang, L., Hu, Y., Ceze, L., Guestrin, C., and Krishnamurthy, A · 2018
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Universal Transformers
Dehghani, M., Gouws, S., Vinyals, O., Uszkoreit, J., and Kaiser, Ł · 2018
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Compiling machine learning programs via high-level tracing
Frostig, R., Johnson, M. J., and Leary, C · 2018
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Graves, A · 2016
Cited alongside, same era.
models/tutorials/image/mnist/convolutional.py
tensorflow · 2016
Cited alongside, same era.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Cited alongside, same era.
Deep learning with dynamic computation graphs
Looks, M., Herreshoff, M., Hutchins, D., and Norvig, P · 2017
Cited alongside, same era.
On-the-fly operation batching in dynamic computation graphs
Neubig, G., Goldberg, Y., and Dyer, C · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
Cited alongside, same era.
Matt Golub · 2018
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Spectral Inference Networks: Unifying Spectral Methods With Deep Learning
Pfau, D., Petersen, S., Agarwal, A., Barrett, D., and Stachenfeld, K · 2018
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Matchbox
Salesforce · 2018
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Dynamic control flow in large-scale machine learning
Yu, Y., Abadi, M., Barham, P., Brevdo, E., Burrows, M., Davis, A., Dean, J., Ghemawat, S., Harley, T., Hawkins, P., Isard, M., Kudlur, M., Monga, R., Murray, D., and Zheng, X · 2018
Later among the works it cites.