Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed in practical applications? Besides the obvious ones–recommendation systems at Pinterest, Alibaba and Twitter–a slightly nuanced success story is the Transformer architecture, which has taken the NLP industry by storm. Through this post, I want to establish links between Graph Neural Networks (GNNs) and Transformers. I’ll talk about the intuitions behind model architectures in the NLP and GNN communities, make connections using equations and figures, and discuss how we could work together to drive progress.
Being highly enthusiastic about research in deep learning I was always searching for unexplored areas in the field (Though it is tough to find one). I had previously worked on Maths word problem…
In the previous blog I explained the theory behind and how a Convolutional Neural Network works for a classification task. Here I will go a step further and touch on techniques used for object…