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TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production.
Compilers and staging transformations
Jørring, U. and Scherlis, W. L · 1986
Earlier work this paper cites.
Partial Evaluation and Automatic Program Generation
Jones, N. D., Gomard, C. K., and Sestoft, P · 1993
Earlier work this paper cites.
Building domain-specific embedded languages
Hudak, P · 1996
Earlier work this paper cites.
Fundamental concepts in programming languages
Strachey, C · 2000
Earlier work this paper cites.
A gentle introduction to multi-stage programming
Taha, W · 2004
Earlier work this paper cites.
Tracing the meta-level: PyPy’s tracing JIT compiler
Bolz, C. F., Cuni, A., Fijalkowski, M., and Rigo, A · 2009
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Theano: A CPU and GPU math compiler in Python
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., and Bengio, Y · 2010
Earlier work this paper cites.
Lightweight modular staging: a pragmatic approach to runtime code generation and compiled DSLs
Rompf, T. and Odersky, M · 2010
Earlier work this paper cites.
Parsing natural scenes and natural language with recursive neural networks
Socher, R., Lin, C. C., Manning, C., and Ng, A. Y · 2011
Earlier work this paper cites.
OptiML: an implicitly parallel domain-specific language for machine learning
Sujeeth, A., Lee, H., Brown, K., Rompf, T., Chafi, H., Wu, M., Atreya, A., Odersky, M., and Olukotun, K · 2011
Earlier work this paper cites.
Terra: A multi-stage language for high-performance computing
DeVito, Z., Hegarty, J., Aiken, A., Hanrahan, P., and Vitek, J · 2013
Earlier work this paper cites.
cuDNN: Efficient Primitives for Deep Learning
Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran, J., Catanzaro, B., and Shelhamer, E · 2014
Cited alongside, same era.
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., and Zhang, Z · 2015
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Segmental Recurrent Neural Networks
Kong, L., Dyer, C., and Smith, N. A · 2015
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Numba: A LLVM-based Python JIT compiler
Lam, S. K., Pitrou, A., and Seibert, S · 2015
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Autograd: Effortless gradients in NumPy
Maclaurin, D., Duvenaud, D., and Adams, R. P · 2015
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Deep learning: The straight dope
The Gluon Team · 2017
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XLA - TensorFlow, compiled
The XLA team · 2017
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DLVM: A modern compiler infrastructure for deep learning systems
Wei, R., Schwartz, L., and Adve, V · 2017
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Automatic differentiation in machine learning: a survey
Baydin, A. G., Pearlmutter, B. A., Radul, A. A., and Siskind, J. M · 2018
Later among the works it cites.
Compiling machine learning programs via high-level tracing
Frostig, R., Johnson, M. J., and Leary, C · 2018
Later among the works it cites.
Flux: Elegant machine learning with Julia
Innes, M · 2018
Later among the works it cites.
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Guide to NumPy
Oliphant, T. E · 2015
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Chainer: a next-generation open source framework for deep learning
Tokui, S., Oono, K., and Hido, S · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
DyNet: The Dynamic Neural Network Toolkit
Neubig, G., Dyer, C., Goldberg, Y., Matthews, A., Ammar, W., Anastasopoulos, A., Ballesteros, M., Chiang, D., Clothiaux, D., Cohn, T., Duh, K., Faruqui, M., Gan, C., Garrette, D., Ji, Y., Kong, L., Kuncoro, A., Kumar, G., Malaviya, C., Michel, P., Oda, Y., Richardson, M., Saphra, N., Swayamdipta, S., and Yin, P · 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.
An in-depth look at Google’s first Tensor Processing Unit (TPU)
Sato, K., Young, C., and Patterson, D · 2017
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On machine learning and programming languages
Innes, M., Karpinski, S., Shah, V., Barber, D., Stenetorp, P., Besard, T., Bradbury, J., Churavy, V., Danisch, S., Edelman, A., Malmaud, J., Revels, J., and Yuret, D · 2018
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Swift for TensorFlow
Lattner, C. and the Swift for TensorFlow Team · 2018
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Generalizing hamiltonian monte carlo with neural networks
Levy, D., Hoffman, M. D., and Sohl-Dickstein, J · 2018
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Torch script
PyTorch team · 2018
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Don’t unroll adjoint: Differentiating SSA-form programs
Innes, M · 2019
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Autograph: Imperative-style coding with graph-based performance
Moldovan, D., Decker, J. M., Wang, F., Johnson, A. A., Lee, B. K., Nado, Z., Sculley, D., Rompf, T., and Wiltschko, A. B · 2019
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