# Feedforward Sequential Memory Neural Networks without Recurrent Feedback

arXiv: https://arxiv.org/abs/1510.02693 · HTML: https://mlsanity.com/paper/1510.02693/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2015.

## Built on by (most cited first, 2 of 2)

- [Large Generative Model-assisted Talking-face Semantic Communication System](https://mlsanity.com/paper/2411.03876/) (2024)
- [Deformable TDNN with adaptive receptive fields for speech recognition](https://mlsanity.com/paper/2104.14791/) (2021)

## Abstract (arXiv)

We introduce a new structure for memory neural networks, called feedforward sequential memory networks (FSMN), which can learn long-term dependency without using recurrent feedback. The proposed FSMN is a standard feedforward neural networks equipped with learnable sequential memory blocks in the hidden layers. In this work, we have applied FSMN to several language modeling (LM) tasks. Experimental results have shown that the memory blocks in FSMN can learn effective representations of long history. Experiments have shown that FSMN based language models can significantly outperform not only feedforward neural network (FNN) based LMs but also the popular recurrent neural network (RNN) LMs.