If you're a deep learning enthusiast you're probably already familiar with some of the basic mathematical primitives that have been driving the impressive capabilities of what we call deep neural networks. Although we like to think of a basic artificial neural network as some nodes with some weighted connections, it's more efficient computationally to think of neural networks as matrix multiplication all the way down. We might draw a cartoon of an artificial neural network like the figure below, with information traveling in from left to right from inputs to outputs (ignoring recurrent networks for now). This type of neural network is a feed-forward multilayer perceptron (MLP). If we want a computer to compute the forward pass for this model, it's going to use a string of matrix multiplies and some sort of non-linearity (here represented by the Greek letter sigma) in the hidden layer: MLPs are well-suited for data that can be naturally shaped as 1D vectors.

Graph deep learning aims to apply deep learning techniques to learn from complex data that is represented as graph.

In this second part of our graph deep learning tutorial, we demonstrate how to move from spectral to spatial domain with convolutions.

From Spectral to Spatial DomainContinue reading on Towards Data Science » Source

Machine learning plays a key role in powering Twitter and our purpose of serving the public conversation. To continually advance the state of machine learning, inside and outside Twitter, we are building out a research group at Twitter, led by Sandeep Pandey, to focus on a few key strategic areas such as natural language processing, reinforcement learning, ML ethics, recommendation systems, and graph deep learning. We are excited to announce that, to help us get there, we have acquired Fabula AI (Fabula), a London-based start-up, with a world-class team of machine learning researchers who employ graph deep learning to detect network manipulation. Graph deep learning is a novel method for applying powerful ML techniques to network-structured data. The result is the ability to analyze very large and complex datasets describing relations and interactions, and to extract signals in ways that traditional ML techniques are not capable of doing.

Twitter announced that it has acquired London-based Fabula AI. The financial terms of transactions are not disclosed. The announcement stated that Twitter has established a research group lead by Sandeep Pandey. The research groups look into areas like natural language processing, reinforcement learning, ML ethics, recommendation systems, and graph deep learning. In one of the posts titled "Fake News revealed through artificial intelligence", it was revealed that Fabula AI team, Michael Bronstein, professor and researcher at the USI Institute of Computational Science (ICS), fellow ICS researchers Federico Monti and Dr Davide Eynard, developed a new method based on algorithms and artificial intelligence that could prove to be the most effective solution to the spreading of fake news through the Internet.