Machine Learning Lab

This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong.
====== FOFE ======

===== Fixed-size Ordinally-Forgetting Encoding (FOFE) =====

{{:projects:fofe:fofe_bigram2.png?0x330 |NN hidden layer}}{{ :projects:fofe:fofe_trigram.png?0x330| HOPE}} \\

\\
 
** FOFE **
is a simple technique to (almost) uniquely map any variable-length sequence into a fixed-size representation, which is particularly suitable for neural networks. It also has an appealing property that the far-away context will be gradually forgotten in the representation, which is good to model natural languages.
\\

We have applied FOFE to feedforward neural network language models (FNN-LMs). Experimental results have shown that without using any recurrent feedbacks, FOFE based FNNLMs can significantly outperform not only the standard fixed-input FNN-LMs but
also the popular RNN-LMs.

**Reference:** 
\\
[1] //S. Zhang, H. Jiang, M. Xu, J. Hou, L. Dai//, "A Fixed-Size Encoding Method  for Variable-Length Sequences with its Application to Neural Network Language Models," [[http://arxiv.org/abs/1505.01504|arXiv:1505.01504]].

[2] //S. Zhang, H. Jiang, M. Xu, J. Hou, L. Dai//, "[[http://anthology.aclweb.org/P/P15/P15-2081.pdf|The Fixed-Size Ordinally-Forgetting Encoding Method for Neural Network Language Models]]," //Proc. of The 53th Annual Meeting of the Association for Computational Linguistics (ACL 2015)//, July 2015.

**Software:**
\\
The matlab codes to reproduce the results in [1,2] can be downloaded {{:projects:fofe:fofe-code.zip|here}}.