【推荐】基于LSTM矩阵分解/分块的大型LSTM网络简化加速（附代码）

论文《Factorization tricks for LSTM networks》摘要：

We present two simple ways of reducing the number of parameters and accelerating the training of large Long Short-Term Memory (LSTM) networks: the first one is "matrix factorization by design" of LSTM matrix into the product of two smaller matrices, and the second one is partitioning of LSTM matrix, its inputs and states into the independent groups. Both approaches allow us to train large LSTM networks significantly faster to the state-of the art perplexity. On the One Billion Word Benchmark we improve single model perplexity down to 24.29.

论文链接：

https://arxiv.org/abs/1703.10722

代码链接：

https://github.com/okuchaiev/f-lm

原文链接：

http://m.weibo.cn/1402400261/4092744978737544