# Efficient Sampled Softmax for Tensorflow

arXiv: https://arxiv.org/abs/2004.05244 · HTML: https://mlsanity.com/paper/2004.05244/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [GIMIRec: Global Interaction Information Aware Multi-Interest Framework for Sequential Recommendation](https://mlsanity.com/paper/2112.08717/) (2021)

## Abstract (arXiv)

This short paper discusses an efficient implementation of sampled softmax loss for Tensorflow. The speedup over the default implementation is achieved due to simplification of the graph for the forward and backward passes.