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I love reading and decoding machine learning research papers.
Since the digital computers are being invented, the human being has been attempting to create machines which directly interact with thereal world without his intervention.
In this sense Artificial Neural Networks comes with an alternative for endowing to the computers one of thecharacteristic that is intelligence.
Finally, the mathematical models involved are presented and demonstrated.
Keywords: component Feed Forward, ANN, sigmoid, distributed nature, firing rules.
That explains why this model is biased towards performing tree-like composition operations.
I wanted to spend a few moments talking about the cumax() function.A tree-structured model can achieve quite a strong performance on this dataset. This process potentially reduces the parameter-counts by more than 90% without affecting the accuracy.It also decreases the size and energy consumption of a trained network, making our inference more efficient.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.This article is my way of giving back to the community that has given me so much!In this article, we’ll look at the two best papers from the ICLR 2019 conference.The ON-LSTM model gives an impressive performance on sequences longer than 3.The ON-LSTM model shows better generalization while facing structured data with various lengths. Pruning is the process of removing unnecessary weights from neural networks.