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
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
Applied machine learning at Facebook: a datacenter infrastructure perspective
Applied machine learning at Facebook: a datacenter infrastructure perspective Hazelwood et al., _HPCA’18 _ This is a wonderful glimpse into what it’s like when machine learning comes to pervade nearly every part of a business, with implications top-to-bottom through the whole stack. It’s amazing to step back and think just how fundamentally software systems have … Continue reading Applied machine learning at Facebook: a datacenter infrastructure perspective
Continuum: a platform for cost-aware low-latency continual learning
Continuum: a platform for cost-aware low-latency continual learning Tian et al., SoCC'18 Let’s start with some broad approximations. Batching leads to higher throughput at the cost of higher latency. Processing items one at a time leads to lower latency and often reduced throughput. We can recover throughput to a degree by throwing horizontally scalable resources … Continue reading Continuum: a platform for cost-aware low-latency continual learning
Rosetta: large scale system for text detection and recognition in images
Rosetta: large scale system for text detection and recognition in images Borisyuk et al., KDD'18 Rosetta is Facebook’s production system for extracting text (OCR) from uploaded images. In the last several years, the volume of photos being uploaded to social media platforms has grown exponentially to the order of hundreds of millions every day, presenting … Continue reading Rosetta: large scale system for text detection and recognition in images
the morning paper 