TensorFlow.js: Machine Learning for the Web and Beyond

no code implementations • 16 Jan 2019 • Daniel Smilkov, Nikhil Thorat, Yannick Assogba, Ann Yuan, Nick Kreeger, Ping Yu, Kangyi Zhang, Shanqing Cai, Eric Nielsen, David Soergel, Stan Bileschi, Michael Terry, Charles Nicholson, Sandeep N. Gupta, Sarah Sirajuddin, D. Sculley, Rajat Monga, Greg Corrado, Fernanda B. Viégas, Martin Wattenberg

TensorFlow. js is a library for building and executing machine learning algorithms in JavaScript.

BIG-bench Machine Learning

GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation

1 code implementation • 5 Sep 2018 • Minsuk Kahng, Nikhil Thorat, Duen Horng Chau, Fernanda Viégas, Martin Wattenberg

Recent success in deep learning has generated immense interest among practitioners and students, inspiring many to learn about this new technology.

SmoothGrad: removing noise by adding noise

20 code implementations • 12 Jun 2017 • Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, Martin Wattenberg

Explaining the output of a deep network remains a challenge.

Interpretable Machine Learning

Embedding Projector: Interactive Visualization and Interpretation of Embeddings

no code implementations • 16 Nov 2016 • Daniel Smilkov, Nikhil Thorat, Charles Nicholson, Emily Reif, Fernanda B. Viégas, Martin Wattenberg

Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications.

BIG-bench Machine Learning Recommendation Systems

Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

4 code implementations • TACL 2017 • Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, Jeffrey Dean

In addition to improving the translation quality of language pairs that the model was trained with, our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation.

Machine Translation NMT +3