IndyLSTMs: Independently Recurrent LSTMs
This work addresses efficiency and overfitting issues in recurrent neural networks for tasks like handwriting recognition, though it is incremental as it modifies an existing architecture.
The authors tackled the problem of reducing parameter count and computational cost in LSTM networks by introducing IndyLSTMs, which use diagonal recurrent weights instead of full matrices, resulting in models that are smaller, faster, and consistently outperform regular LSTMs in accuracy per parameter and overall accuracy on handwriting datasets.
We introduce Independently Recurrent Long Short-term Memory cells: IndyLSTMs. These differ from regular LSTM cells in that the recurrent weights are not modeled as a full matrix, but as a diagonal matrix, i.e.\ the output and state of each LSTM cell depends on the inputs and its own output/state, as opposed to the input and the outputs/states of all the cells in the layer. The number of parameters per IndyLSTM layer, and thus the number of FLOPS per evaluation, is linear in the number of nodes in the layer, as opposed to quadratic for regular LSTM layers, resulting in potentially both smaller and faster models. We evaluate their performance experimentally by training several models on the popular \iamondb and CASIA online handwriting datasets, as well as on several of our in-house datasets. We show that IndyLSTMs, despite their smaller size, consistently outperform regular LSTMs both in terms of accuracy per parameter, and in best accuracy overall. We attribute this improved performance to the IndyLSTMs being less prone to overfitting.