Fetching the paper…
Reading the bibliography…
There is a perceived trade-off between machine learning code that is easy to write, and machine learning code that is scalable or fast to execute.
Lightweight modular staging: a pragmatic approach to runtime code generation and compiled dsls
Rompf, T. and Odersky, M · 2010
Earlier work this paper cites.
Julia: A fast dynamic language for technical computing
Bezanson, J., Karpinski, S., Shah, V. B., and Edelman, A · 2012
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.
Python: the full monty
Politz, J. G., Martinez, A., Milano, M., Warren, S., Patterson, D., Li, J., Chitipothu, A., and Krishnamurthi, S · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., and Potts, C · 2013
Earlier work this paper cites.
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
Earlier work this paper cites.
Numba: A llvm-based python jit compiler
Lam, S. K., Pitrou, A., and Seibert, S · 2015
Earlier work this paper cites.
Autograd: Effortless gradients in numpy
Maclaurin, D., Duvenaud, D., and Adams, R. P · 2015
Earlier work this paper cites.
Improved semantic representations from tree-structured long short-term memory networks
Tai, K. S., Socher, R., and Manning, C. D · 2015
Earlier work this paper cites.
Chainer: a next-generation open source framework for deep learning
Tokui, S., Oono, K., Hido, S., and Clayton, J · 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., et al · 2016
Cited alongside, same era.
Theano: A python framework for fast computation of mathematical expressions
Al-Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A., Belopolsky, A., et al · 2016
Cited alongside, same era.
Precise, dynamic information flow for database-backed applications
Yang, J., Hance, T., Austin, T. H., Solar-Lezama, A., Flanagan, C., and Chong, S · 2016
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Cited alongside, same era.
Open neural network exchange
ONNX Contributors · 2018
Closest in time.
Torch script
PyTorch Contributors · 2018
Closest in time.
torch-autograd
Torch Autograd Contributors · 2018
Closest in time.
Automatic differentiation in ml: Where we are and where we should be going
van Merrienboer, B., Breuleux, O., Bergeron, A., and Lamblin, P · 2018
Closest in time.
A language and compiler view on differentiable programming, 2018
Wang, F. and Rompf, T · 2018
Closest in time.
Demystifying differentiable programming: Shift/reset the penultimate backpropagator
Wang, F., Wu, X., Essertel, G. M., Decker, J. M., and Rompf, T · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Tangent: automatic differentiation using source code transformation in python
van Merriënboer, B., Wiltschko, A. B., and Moldovan, D · 2017
Cited alongside, same era.
Deep learning framework power scores 2018
Hale, J · 2018
Cited alongside, same era.
Chainer: a next-generation open source framework for deep learning
Tokui, S., Oono, K., Hido, S., and Clayton, J
Cited in the paper.
The 800 pound python in the machine learning room
Decker, J. M., Moldovan, D., Wei, G., Bhardwaj, V., Essertel, G., Wang, F., Wiltschko, A. B., and Rompf, T · 2019
Closest in time.
JANUS: Fast and flexible deep learning via symbolic graph execution of imperative programs
Jeong, E., Cho, S., Yu, G.-I., Jeong, J. S., Shin, D.-J., and Chun, B.-G · 2019
Closest in time.