TensorFlow: learning functions at scale

By Martín Abadi. ACM SIGPLAN International Conference on Functional Programming, 2016

431 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/a620d007603111ae263c5769c9dc9ac37efd2ddb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Xin Gao
2. Ming Fan
3. Ramzan Umarov
4. Rajat Sen
5. Andrew B. Leach
6. Bingqing Xie
7. Shaan Mathur
8. Weihao Kong

In Materials Chemistry

1. Xin Gao
2. Ming Fan
3. Ramzan Umarov
4. Rajat Sen
5. Andrew B. Leach
6. Bingqing Xie
7. Shaan Mathur
8. Weihao Kong

Citations in the corpus

• DEEPre: sequence-based enzyme EC number prediction by deep learning (2017, methodology): “In practice, we use TensorFlow (Abadi, 2016) as the framework to construct the deep neural network.”
• Long-term Forecasting with TiDE: Time-series Dense Encoder (2023, methodology): “Our model is trained in Tensorflow (Abadi, 2016) and we optimize using the default settings of the Adam optimizer (Kingma and Ba, 2014).”