mlsanityData to 7 Oct 2026

Swift for TensorFlow: A portable, flexible platform for deep learning

arXiv 2102.13243 ↗First posted 26 Feb 2021cs.LG cs.DCcited by 6

Not on the map. Why?

Abstract (arXiv)

Swift for TensorFlow is a deep learning platform that scales from mobile devices to clusters of hardware accelerators in data centers. It combines a language-integrated automatic differentiation system and multiple Tensor implementations within a modern ahead-of-time compiled language oriented around mutable value semantics. The resulting platform has been validated through use in over 30 deep learning models and has been employed across data center and mobile applications.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 17 of its 48 references that are in the map. First posted on arXiv 26 Feb 2021.

Builds on

17 of 48 references.

References · 17

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  4. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  5. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  6. 2017In-Datacenter Performance Analysis of a Tensor Processing Unitcited by 610
  7. 2019OpenSpiel: A Framework for Reinforcement Learning in Gamescited by 180
  8. 2019MLPerf Training Benchmarkcited by 114
  9. 2018Glow: Graph Lowering Compiler Techniques for Neural Networkscited by 73
  10. 2020Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradientscited by 26
  11. 2019TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learningcited by 22
  12. 2019Chainer: A Deep Learning Framework for Accelerating the Research Cyclecited by 22
5 more
  1. 2017DyNet: The Dynamic Neural Network Toolkitcited by 21
  2. 2020Jelly Bean World: A Testbed for Never-Ending Learningcited by 19
  3. 2018Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagatorcited by 14
  4. 2018Automatic Full Compilation of Julia Programs and ML Models to Cloud TPUscited by 3
  5. 2020Automatic Differentiation in ROOTcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 1.

6 papers.

  1. 2021Equinox: neural networks in JAX via callable PyTrees and filtered transformationscited by 143
  2. 2021Torch.fx: Practical Program Capture and Transformation for Deep Learning in Pythoncited by 38
  3. 2021An automatic differentiation system for the age of differential privacycited by 2
  4. 2022Machines of finite depth: towards a formalization of neural networkscited by 2
  5. 2021Probabilistic Tracking with Deep Factorscited by 0
  6. 2026Differentiate the Evaluator, Not the Program: An Efficient Runtime Representation for Neuro-Symbolic Learningcited by 0