PyTorch-BigGraph: a large-scale graph embedding system Lerer et al., SysML'19 We looked at graph neural networks earlier this year, which operate directly over a graph structure. Via graph autoencoders or other means, another approach is to learn embeddings for the nodes in the graph, and then use these embeddings as inputs into a (regular) neural … Continue reading PyTorch-BigGraph: a large-scale graph embedding system

# Tag: Machine Learning

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

# Towards federated learning at scale: system design

Towards federated learning at scale: system design Bonawitz et al., SysML 2019 This is a high level paper describing Google’s production system for federated learning. One of the most interesting things to me here is simply to know that Google are working on this, have a first version in production working with tens of millions … Continue reading Towards federated learning at scale: system design

# Data validation for machine learning

Data validation for machine learning Breck et al., SysML'19 Last time out we looked at continuous integration testing of machine learning models, but arguably even more important than the model is the data. Garbage in, garbage out. In this paper we focus on the problem of validation the input data fed to ML pipelines. The … Continue reading Data validation for machine learning

# Continuous integration of machine learning models with ease.ml/ci

Continuous integration of machine learning models with ease.ml/ci: towards a rigorous yet practical treatment Renggli et al., SysML'19 Developing machine learning models is no different from developing traditional software, in the sense that it is also a full life cycle involving design, implementation, tuning, testing, and deployment. As machine learning models are used in more … Continue reading Continuous integration of machine learning models with ease.ml/ci

# Boosted race trees for low energy classification

Boosted race trees for low energy classification Tzimpragos et al., ASPLOS'19 We don’t talk about energy as often as we probably should on this blog, but it’s certainly true that our data centres and various IT systems consume an awful lot of it. So it’s interesting to see a paper using nano-Joules per prediction as … Continue reading Boosted race trees for low energy classification

# The why and how of nonnegative matrix factorization

The why and how of nonnegative matrix factorization Gillis, arXiv 2014 from: ‘Regularization, Optimization, Kernels, and Support Vector Machines.’ Last week we looked at the paper ‘Beyond news content,’ which made heavy use of nonnegative matrix factorisation. Today we’ll be looking at that technique in a little more detail. As the name suggests, ‘The Why … Continue reading The why and how of nonnegative matrix factorization

# Graph neural networks: a review of methods and applications

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