Graph neural networks: a review of methods and applications Zhou et al., arXiv 2019 It’s another graph neural networks survey paper today! Cue the obligatory bus joke. Clearly, this covers much of the same territory as we looked at earlier in the week, but when we’re lucky enough to get two surveys published in short … Continue reading Graph neural networks: a review of methods and applications

# Tag: Machine Learning

The machine learning subset of AI. Includes deep learning among other topics.

# A comprehensive survey on graph neural networks

A comprehensive survey on graph neural networks Wu et al., arXiv'19 Last year we looked at ‘Relational inductive biases, deep learning, and graph networks,’ where the authors made the case for deep learning with structured representations, which are naturally represented as graphs. Today’s paper choice provides us with a broad sweep of the graph neural … Continue reading A comprehensive survey on graph neural networks

# TensorFlow.js: machine learning for the web and beyond

TensorFlow.js: machine learning for the web and beyond Smilkov et al., SysML'19 If machine learning and ML models are to pervade all of our applications and systems, then they’d better go to where the applications are rather than the other way round. Increasingly, that means JavaScript - both in the browser and on the server. … Continue reading TensorFlow.js: machine learning for the web and beyond

# Towards a hands-free query optimizer through deep learning

Towards a hands-free query optimizer through deep learning Marcus & Papaemmanouil, CIDR'19 Where the SageDB paper stopped— at the exploration of learned models to assist in query optimisation— today’s paper choice picks up, looking exclusively at the potential to apply learning (in this case deep reinforcement learning) to build a better optimiser. Why reinforcement learning? … Continue reading Towards a hands-free query optimizer through deep learning

# SageDB: a learned database system

SageDB: a learned database system Kraska et al., CIDR'19 About this time last year, a paper entitled ‘The case for learned index structures’ (part I, part II) generated a lot of excitement and debate. Today’s paper choice builds on that foundation, putting forward a vision where learned models pervade every aspect of a database system. … Continue reading SageDB: a learned database system

# Neural Ordinary Differential Equations

Neural ordinary differential equations Chen et al., NeurIPS'18 ‘Neural Ordinary Differential Equations’ won a best paper award at NeurIPS last month. It’s not an easy piece (at least not for me!), but in the spirit of ‘deliberate practice’ that doesn’t mean there isn’t something to be gained from trying to understand as much as possible. … Continue reading Neural Ordinary Differential Equations

# The tradeoffs of large scale learning

The tradeoffs of large scale learning Bottou & Bousquet, NIPS'07 Welcome to another year of The Morning Paper. As usual we’ll be looking at a broad cross-section of computer science research (I have over 40 conferences/workshops on my list to keep an eye on as a start!). I’ve no idea yet what papers we’ll stumble … Continue reading The tradeoffs of large scale learning