Fetching the paper…
Reading the bibliography…
Swift for TensorFlow is a deep learning platform that scales from mobile devices to clusters of hardware accelerators in data centers.
OpenSpiel: A framework for reinforcement learning in games
Lanctot, M., Lockhart, E., Lespiau, J.-B., Zambaldi, V., Upadhyay, S., Pérolat, J., Srinivasan, S., Timbers, F., Tuyls, K., Omidshafiei, S., Hennes, D., Morrill, D., Muller, P., Ewalds, T., Faulkner, R., Kramár, J., Vylder, B. D., Saeta, B., Bradbury, J., Ding, D., Borgeaud, S., Lai, M., Schrittwieser, J., Anthony, T., Hughes, E., Danihelka, I., and Ryan-Davis, J · 1908
Earlier work this paper cites.
Making data structures persistent, 1989
Driscoll, J. R., Sarnak, N., Sleator, D. D., and Tarjan, R. E · 1989
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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.
The genuine sieve of Eratosthenes
O’Neill, M · 2009
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.
The Tapenade automatic differentiation tool: principles, model, and specification
Laurent Hascoet, V. P · 2013
Earlier work this paper cites.
Autograd: Effortless gradients in NumPy
Maclaurin, D., Duvenaud, D., and Adams, R. P · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Modeling, inference and optimization with composable differentiable procedures
Maclaurin, D · 2016
Earlier work this paper cites.
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
XLA: TensorFlow, compiled
Leary, C. and Wang, T · 2017
Earlier work this paper cites.
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
Cited alongside, same era.
AI and Compute, 2018
Amodei, D., Hernandez, D., Sastry, G., Clark, J., Brokman, G., and Sutskever, I · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Cited alongside, same era.
Flare: Optimizing Apache Spark with native compilation for scale-up architectures and medium-size data
Essertel, G., Tahboub, R., Decker, J., Brown, K., Olukotun, K., and Rompf, T · 2018
Alphastar: Mastering the real-time strategy game StarCraft II
Vinyals, O., Babuschkin, I., Chung, J., Mathieu, M., Jaderberg, M., Czarnecki, W. M., Dudzik, A., Huang, A., Georgiev, P., Powell, R., et al · 2019
Later among the works it cites.
The differentiable curry
Vytiniotis, D., Belov, D., Wei, R., Plotkin, G., and Abadi, M · 2019
Later among the works it cites.
Demystifying differentiable programming: Shift/reset the penultimate backpropagator
Wang, F., Zheng, D., Decker, J., Wu, X., Essertel, G. M., and Rompf, T · 2019
Later among the works it cites.
Differentiable programming manifesto
Wei, R., Zheng, D., Rasi, M., and Chrzasczcz, B · 2019
Later among the works it cites.
JAX: composable transformations of Python + NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., and Wanderman-Milne, S · 2020
Later among the works it cites.
LazyTensor: A portable approach to combine eager execution and fusing compilers, 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Automatic full compilation of Julia programs and ML models to Cloud TPUs
Fischer, K. and Saba, E · 2018
Cited alongside, same era.
Compiling machine learning programs via high-level tracing
Frostig, R., Johnson, M. J., and Leary, C · 2018
Cited alongside, same era.
Glow: Graph lowering compiler techniques for neural networks
Rotem, N., Fix, J., Abdulrasool, S., Deng, S., Dzhabarov, R., Hegeman, J., Levenstein, R., Maher, B., Satish, N., Olesen, J., Park, J., Rakhov, A., and Smelyanskiy, M · 2018
Cited alongside, same era.
TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learning
Agrawal, A., Modi, A. N., Passos, A., Lavoie, A., Agarwal, A., Shankar, A., Ganichev, I., Levenberg, J., Hong, M., Monga, R., et al · 2019
Cited alongside, same era.
Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems
Géron, A · 2019
Cited alongside, same era.
Zygote: A differentiable programming system to bridge machine learning and scientific computing
Innes, M., Edelman, A., Fischer, K., Rackauckus, C., Saba, E., Shah, V. B., and Tebbutt, W · 2019
Cited alongside, same era.
Dex: array programming with typed indices
Maclaurin, D., Radul, A., Johnson, M. J., and Vytiniotis, D · 2019
Cited alongside, same era.
Şuhan, A., Libenzi, D., Zhang, A., Schuh, P., Saeta, B., Sohn, J. Y., and Shabalin, D · 2020
Later among the works it cites.
Array programming with NumPy
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al · 2020
Later among the works it cites.
Flax: A neural network library for jax designed for flexibility
Heek, J., Levskaya, A., Oliver, A., Pop, R., Reddy, M., Rondepierre, B., Suo, D., and van Zee, M · 2020
Later among the works it cites.
Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD
Howard, J. and Gugger, S · 2020
Later among the works it cites.
A domain-specific supercomputer for training deep neural networks
Jouppi, N., Yoon, D., Kurian, G., Li, S., Patil, N., Laudon, J., Young, C., and Patterson, D · 2020
Later among the works it cites.
MLPerf training benchmark
Mattson, P., Cheng, C., Diamos, G. F., Coleman, C., Micikevicius, P., Patterson, D. A., Tang, H., Wei, G., Bailis, P., Bittorf, V., Brooks, D., Chen, D., Dutta, D., Gupta, U., Hazelwood, K. M., Hock, A., Huang, X., Kang, D., Kanter, D., Kumar, N., Liao, J., Narayanan, D., Oguntebi, T., Pekhimenko, G., Pentecost, L., Reddi, V. J., Robie, T., John, T. S., Wu, C., Xu, L., Young, C., and Zaharia, M · 2020
Later among the works it cites.
Instead of rewriting foreign code for machine learning, automatically synthesize fast gradients, 2020
Moses, W. S. and Churavy, V · 2020
Later among the works it cites.
Tensors Fitting Perfectly, 2020
Paszke, A. and Saeta, B · 2020
Later among the works it cites.
Jelly bean world: A testbed for never-ending learning
Platanios, E. A., Saparov, A., and Mitchell, T · 2020
Later among the works it cites.
Automatic differentiation in ROOT, 2020
Vassilev, V., Efremov, A., and Shadura, O · 2020
Later among the works it cites.