Recurrent methods, Attention, and Gates for Geometric Deep Learning
Graph Learning and Geometric Deep Learning — Part 3
Be sure to familiarize yourself with deep learning and graph learning, for some background and prerequisites. Also look into Geometric Deep Learning as a field before diving into recurrent methods in graph learning.
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Not long ago, Deep Learning and Machine Learning exploded in popularity. While there is much more history pre-2000s that contributed to the contributions in the 2010s, it was the landmark 2009 paper on Recurrent Neural Networks by Yoshua Bengio that would become the basis for the modern LSTM.
Coupled with the rediscovery of back-propagation and GPUs with big data, as well as a breakout string of victories in image analysis using Convolutional Neural Networks in 2012, the hype train was well on its way. Today, recurrent models are used in financial markets, smart-home technologies, and computer-assisted speech.
Contrary to the rest of Deep Learning, not much is known about the history of Graph Learning and Geometric Deep Learning. What we do know:
- In 2005, a team of researchers used Deep Learning to learn from…
