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Modern deep learning frameworks provide imperative, eager execution programming interfaces embedded in Python to provide a productive development experience.
TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning
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Deep Learning Recommendation Model for Personalization and Recommendation Systems
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Chainer: A Deep Learning Framework for Accelerating the Research Cycle
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Graphviz— Open Source Graph Drawing Tools
Ellson, J., Gansner, E., Koutsofios, L., North, S. C., and Woodhull, G · 2002
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MLIR: A Compiler Infrastructure for the End of Moore’s Law
Lattner, C., Pienaar, J. A., Amini, M., Bondhugula, U., Riddle, R., Cohen, A., Shpeisman, T., Davis, A., Vasilache, N., and Zinenko, O · 2002
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Protocolbuffers/Protobuf: Protocol buffers - google’s data interchange format, 2008
Xiao, F. et al · 2008
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Lightweight modular staging: a pragmatic approach to runtime code generation and compiled DSLs
Rompf, T. and Odersky, M · 2010
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Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gülçehre, Ç., Bougares, F., Schwenk, H., and Bengio, Y · 2014
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Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R. B., Guadarrama, S., and Darrell, T · 2014
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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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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Autograd: Effortless Gradients in Numpy
Maclaurin, D., Duvenaud, D., and Adams, R. P · 2015
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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
TorchScript, Sep 2018
DeVito, Z. et al · 2018
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Automatic Full Compilation of Julia Programs and ML Models to Cloud TPUs
Fischer, K. and Saba, E · 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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Don’t Unroll Adjoint: Differentiating SSA-Form Programs
Innes, M · 2018
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Quantizing deep convolutional networks for efficient inference: A whitepaper
Krishnamoorthi, R · 2018
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Theano: A Python framework for fast computation of mathematical expressions
Al-Rfou, R., Alain, G., Almahairi, A., Angermüller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A., Belopolsky, A., Bengio, Y., Bergeron, A., Bergstra, J., Bisson, V., Snyder, J. B., Bouchard, N., Boulanger-Lewandowski, N., Bouthillier, X., de Brébisson, A., Breuleux, O., Carrier, P. L., Cho, K., Chorowski, J., Christiano, P. F., Cooijmans, T., Côté, M., Côté, M., Courville, A. C., Dauphin, Y. N., Delalleau, O., Demouth, J., Desjardins, G., Dieleman, S., Dinh, L., Ducoffe, M., Dumoulin, V., Kahou, S. E., Erhan, D., Fan, Z., Firat, O., Germain, M., Glorot, X., Goodfellow, I. J., Graham, M., Gülçehre, Ç., Hamel, P., Harlouchet, I., Heng, J., Hidasi, B., Honari, S., Jain, A., Jean, S., Jia, K., Korobov, M., Kulkarni, V., Lamb, A., Lamblin, P., Larsen, E., Laurent, C., Lee, S., Lefrançois, S., Lemieux, S., Léonard, N., Lin, Z., Livezey, J. A., Lorenz, C., Lowin, J., Ma, Q., Manzagol, P., Mastropietro, O., McGibbon, R., Memisevic, R., van Merriënboer, B., Michalski, V., Mirza, M., Orlandi, A., Pal, C. J., Pascanu, R., Pezeshki, M., Raffel, C., Renshaw, D., Rocklin, M., Romero, A., Roth, M., Sadowski, P., Salvatier, J., Savard, F., Schlüter, J., Schulman, J., Schwartz, G., Serban, I. V., Serdyuk, D., Shabanian, S., Simon, É., Spieckermann, S., Subramanyam, S. R., Sygnowski, J., Tanguay, J., van Tulder, G., Turian, J. P., Urban, S., Vincent, P., Visin, F., de Vries, H., Warde-Farley, D., Webb, D. J., Willson, M., Xu, K., Xue, L., Yao, L., Zhang, S., and Zhang, Y · 2016
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Julia: A fresh approach to numerical computing
Bezanson, J., Edelman, A., Karpinski, S., and Shah, V. B · 2017
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On machine learning and programming languages, Dec 2017
Innes, M. et al · 2017
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Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A. G., Adam, H., and Kalenichenko, D · 2017
Cited alongside, same era.
In-datacenter performance analysis of a tensor processing unit
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al · 2017
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Training deep autoencoders for collaborative filtering, 2017
Kuchaiev, O. and Ginsburg, B · 2017
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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., et al · 2017
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Markuš, N · 2018
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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 · 2018
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Improving subclassing Tensor by propagating subclass instances, Aug 2020
Abbasi, H., Yang, E. Z., and Gommers, R · 2020
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Flax: A neural network library and ecosystem for JAX, 2020
Heek, J., Levskaya, A., Oliver, A., Ritter, M., Rondepierre, B., Steiner, A., and van Zee, M · 2020
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Haiku: Sonnet for JAX, 2020
Hennigan, T., Cai, T., Norman, T., and Babuschkin, I · 2020
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(Beta) building a convolution/batch norm fuser in fx, Mar 2021
He, H · 2021
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FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference
Khudia, D. S., Huang, J., Basu, P., Deng, S., Liu, H., Park, J., and Smelyanskiy, M · 2021
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Swift for TensorFlow: A portable, flexible platform for deep learning
Saeta, B., Shabalin, D., Rasi, M., Larson, B., Wu, X., Schuh, P., Casbon, M., Zheng, D., Abdulrasool, S., Efremov, A., Abrahams, D., Lattner, C., and Wei, R · 2021
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LazyTensor: combining eager execution with domain-specific compilers
Suhan, A., Libenzi, D., Zhang, A., Schuh, P., Saeta, B., Sohn, J. Y., and Shabalin, D · 2021
Closest in time.