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It is the key to unlocking the approach presented in this awesome paper.This cumax() activation function is introduced to enforce an order to the update frequency: g^= cumax(…) = cumsum(softmax(…)), Here, cumsum denotes the cumulative sum.The ON-LSTM model gives an impressive performance on sequences longer than 3.
The values in the master forget gate monotonically increase from 0 to 1 by following the properties of the cumax() function.
A similar thing happens in the master input gate where the values monotonically decrease from 1 to 0.
The researchers aim to integrate a tree structure into a neural network language model.
The reason behind doing this is to improve generalization via better inductive bias and at the same time, potentially reduce the need for a large amount of training data.
Finally, the mathematical models involved are presented and demonstrated.
Keywords: component Feed Forward, ANN, sigmoid, distributed nature, firing rules.
So, the researchers have proposed to make the gate for each neuron dependent on the others by enforcing the order in which neurons should be updated. ON-LSTM includes a new gating mechanism and a new activation function cumax().
The cumax() function and LSTM are combined together to create a new model ON-LSTM.
That’s why I decided to help my fellow data scientists in understanding these research papers.
There are so many incredible academic conferences happening these days and we need to keep ourselves updated with the latest machine learning developments.