Machine Learning

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

The why and how of nonnegative matrix factorization

~ Adrian Colyer ~ 14 Comments
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

Towards a hands-free query optimizer through deep learning

~ Adrian Colyer ~ 3 Comments
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

Applied machine learning at Facebook: a datacenter infrastructure perspective

~ Adrian Colyer ~ 4 Comments
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

Continuum: a platform for cost-aware low-latency continual learning

~ Adrian Colyer ~ 3 Comments
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