# TensorFlow.js: Machine Learning for the Web and Beyond

arXiv: https://arxiv.org/abs/1901.05350 · HTML: https://mlsanity.com/paper/1901.05350/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2019.

## Built on by (most cited first, 29 of 29)

- [Evaluating the Interpretability of Generative Models by Interactive Reconstruction](https://mlsanity.com/paper/2102.01264/) (2021)
- [Widening Access to Applied Machine Learning with TinyML](https://mlsanity.com/paper/2106.04008/) (2021)
- [WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19](https://mlsanity.com/paper/2101.00784/) (2021)
- [GriddlyJS: A Web IDE for Reinforcement Learning](https://mlsanity.com/paper/2207.06105/) (2022)
- [EL-VIT: Probing Vision Transformer with Interactive Visualization](https://mlsanity.com/paper/2401.12666/) (2024)
- [Speed Benchmarking of Genetic Programming Frameworks](https://mlsanity.com/paper/2106.11919/) (2021)
- [Explainability via Interactivity? Supporting Nonexperts' Sensemaking of Pretrained CNN by Interacting with Their Daily Surroundings](https://mlsanity.com/paper/2107.01996/) (2021)
- [Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries Across Software Package Ecosystems](https://mlsanity.com/paper/2201.07201/) (2022)
- [Dressi: A Hardware-Agnostic Differentiable Renderer with Reactive Shader Packing and Soft Rasterization](https://mlsanity.com/paper/2204.01386/) (2022)
- [Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction](https://mlsanity.com/paper/2204.11382/) (2022)
- [WebSHAP: Towards Explaining Any Machine Learning Models Anywhere](https://mlsanity.com/paper/2303.09545/) (2023)
- [Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks](https://mlsanity.com/paper/2307.05318/) (2023)
- [Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges](https://mlsanity.com/paper/2308.12438/) (2023)
- [WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy](https://mlsanity.com/paper/2110.11646/) (2021)
- [Deep Learning-Based Sparse Whole-Slide Image Analysis for the Diagnosis of Gastric Intestinal Metaplasia](https://mlsanity.com/paper/2201.01449/) (2022)
- [Deep Smart Contract Intent Detection](https://mlsanity.com/paper/2211.10724/) (2022)
- [Exploring the Power of Creative AI Tools and Game-Based Methodologies for Interactive Web-Based Programming](https://mlsanity.com/paper/2308.11649/) (2023)
- [Brainchop: Next Generation Web-Based Neuroimaging Application](https://mlsanity.com/paper/2310.16162/) (2023)
- [DistML.js: Installation-free Distributed Deep Learning Framework for Web Browsers](https://mlsanity.com/paper/2407.01023/) (2024)
- [PyMilo: A Python Library for ML I/O](https://mlsanity.com/paper/2501.00528/) (2024)
- [An Evolution of CNN Object Classifiers on Low-Resolution Images](https://mlsanity.com/paper/2101.00686/) (2021)
- [C3DRec: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Era](https://mlsanity.com/paper/2101.05641/) (2021)
- [Progressive Transmission and Inference of Deep Learning Models](https://mlsanity.com/paper/2110.00916/) (2021)
- [Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques](https://mlsanity.com/paper/2407.00120/) (2024)
- [TMA-Grid: An open-source, zero-footprint web application for FAIR Tissue MicroArray De-arraying](https://mlsanity.com/paper/2407.21233/) (2024)
- [asanAI: In-Browser, No-Code, Offline-First Machine Learning Toolkit](https://mlsanity.com/paper/2501.06226/) (2025)
- [InFL-UX: A Toolkit for Web-Based Interactive Federated Learning](https://mlsanity.com/paper/2503.04318/) (2025)
- [WebSerial Vision Training for Microcontrollers: A Browser-Based Companion to On-Device CNN Training](https://mlsanity.com/paper/2604.22834/) (2026)
- [Meganeura: Portable GPU Training and Inference through Vulkan and Metal](https://mlsanity.com/paper/2608.01563/) (2026)

## Abstract (arXiv)

TensorFlow.js is a library for building and executing machine learning algorithms in JavaScript. TensorFlow.js models run in a web browser and in the Node.js environment. The library is part of the TensorFlow ecosystem, providing a set of APIs that are compatible with those in Python, allowing models to be ported between the Python and JavaScript ecosystems. TensorFlow.js has empowered a new set of developers from the extensive JavaScript community to build and deploy machine learning models and enabled new classes of on-device computation. This paper describes the design, API, and implementation of TensorFlow.js, and highlights some of the impactful use cases.