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Frameworks for writing, compiling, and optimizing deep learning (DL) models have recently enabled progress in areas like computer vision and natural language processing.
Recent trends in deep learning based natural language processing
Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria · 1904
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The function of function in lisp or why the funarg problem should be called the environment problem
Joel Moses · 1970
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A proposed solution to the funarg problem
Erik Sandewall · 1971
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Partial evaluation for higher-order languages with state
Peter Thiemann and Dirk Dussart · 1996
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Abstraction and the C++ machine model
Bjarne Stroustrup · 2004
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Theano: A cpu and gpu math compiler in python
James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, and Yoshua Bengio · 2010
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Exploiting vector instructions with generalized stream fusio
Geoffrey Mainland, Roman Leshchinskiy, and Simon Peyton Jones · 2013
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Playing atari with deep reinforcement learning, 2013
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Halide: A language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines
Jonathan Ragan-Kelley, Connelly Barnes, Andrew Adams, Sylvain Paris, Frédo Durand, and Saman Amarasinghe · 2013
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Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross B. Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Automatic differentiation in machine learning: a survey
Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind · 2015
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Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang · 2015
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The End of Error: Unum Computing
J.L. Gustafson · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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The unreasonable effectiveness of recurrent neural networks
Andrej Karpathy · 2015
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Kumar Divvala, Ross B. Girshick, and Ali Farhadi · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Improved semantic representations from tree-structured long short-term memory networks
Kai Sheng Tai, Richard Socher, and Christopher D. Manning · 2015
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Chainer: a next-generation open source framework for deep learning
Seiya Tokui, Kenta Oono, Shohei Hido, and Justin Clayton · 2015
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Tensorflow: A system for large-scale machine learning
Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
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Semantic object parsing with graph LSTM
Xiaodan Liang, Xiaohui Shen, Jiashi Feng, Liang Lin, and Shuicheng Yan · 2016
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https://www.apple.com/newsroom/2017/09/the-future-is-here-iphone-x/ , 2017
Apple · 2017
Intel ngraph: An intermediate representation, compiler, and executor for deep learning
Scott Cyphers, Arjun K. Bansal, Anahita Bhiwandiwalla, Jayaram Bobba, Matthew Brookhart, Avijit Chakraborty, William Constable, Christian Convey, Leona Cook, Omar Kanawi, Robert Kimball, Jason Knight, Nikolay Korovaiko, Varun Kumar Vijay, Yixing Lao, Christopher R. Lishka, Jaikrishnan Menon, Jennifer Myers, Sandeep Aswath Narayana, Adam Procter, and Tristan J. Webb · 2018
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Diesel: Dsl for linear algebra and neural net computations on gpus
Venmugil Elango, Norm Rubin, Mahesh Ravishankar, Hariharan Sandanagobalane, and Vinod Grover · 2018
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Flux: Elegant machine learning with julia
Mike Innes · 2018
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Quantizing deep convolutional networks for efficient inference: A whitepaper
Raghuraman Krishnamoorthi · 2018
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Jax: Autograd and xla
Google LLC · 2018
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Announcing tensorflow fold: Deep learning with dynamic computation graphs
Moshe Looks, Marcello Herreshoff, and DeLesley Hutchins · 2017
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Deep learning with dynamic computation graphs
Moshe Looks, Marcello Herreshoff, DeLesley Hutchins, and Peter Norvig · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Generating names with a character-level rnn
Sean Robertson · 2017
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Developing bug-free machine learning systems with formal mathematics
Daniel Selsam, Percy Liang, and David L. Dill · 2017
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Eager execution: An imperative, define-by-run interface to tensorflow
Asim Shankar and Wolff Dobson · 2017
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Autograph: Imperative-style coding with graph-based performance
Dan Moldovan, James M Decker, Fei Wang, Andrew A Johnson, Brian K Lee, Zachary Nado, D Sculley, Tiark Rompf, and Alexander B Wiltschko · 2018
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Glow: Graph lowering compiler techniques for neural networks
Nadav Rotem, Jordan Fix, Saleem Abdulrasool, Summer Deng, Roman Dzhabarov, James Hegeman, Roman Levenstein, Bert Maher, Satish Nadathur, Jakob Olesen, Jongsoo Park, Artem Rakhov, and Misha Smelyanskiy · 2018
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Swift for tensorflow
TensorFlow Team · 2018
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Torchscript documentation
Torch Team · 2018
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Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming
Bart van Merrienboer, Dan Moldovan, and Alexander Wiltschko · 2018
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Tensor comprehensions: Framework-agnostic high-performance machine learning abstractions, 2018
Nicolas Vasilache, Oleksandr Zinenko, Theodoros Theodoridis, Priya Goyal, Zachary DeVito, William S. Moses, Sven Verdoolaege, Andrew Adams, and Albert Cohen · 2018
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Demystifying differentiable programming: Shift/reset the penultimate backpropagator
Fei Wang, Xilun Wu, Grégory M. Essertel, James M. Decker, and Tiark Rompf · 2018
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Gluon model zoo
Gluon Team · 2019
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Tensorflow lite supported datatypes, 2019
Google · 2019
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Onnc: A compilation framework connecting onnx to proprietary deep learning accelerators
W. Lin, D. Tsai, L. Tang, C. Hsieh, C. Chou, P. Chang, and L. Hsu · 2019
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A hardware-software blueprint for flexible deep learning specialization
T. Moreau, T. Chen, L. Vega, J. Roesch, L. Zheng, E. Yan, J. Fromm, Z. Jiang, L. Ceze, C. Guestrin, and A. Krishnamurthy · 2019
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Quantization in glow
PyTorch Team · 2019
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