150 successful machine learning models: 6 lessons learned at Booking.com

150 successful machine learning models: 6 lessons learned at Booking.com Bernadi et al., KDD'19 Here’s a paper that will reward careful study for many organisations. We’ve previously looked at the deep penetration of machine learning models in the product stacks of leading companies, and also some of the pre-requisites for being successful with it. Today’s ... Continue Reading

Declarative recursive computation on an RDBMS

Declarative recursive computation on an RDBMS... or, why you should use a database for distributed machine learing Jankov et al., VLDB'19 If you think about a system like Procella that’s combining transactional and analytic workloads on top of a cloud-native architecture, extensions to SQL for streaming, dataflow based materialized views (see e.g. Naiad, Noria, Multiverses, ... Continue Reading

Challenging common assumptions in the unsupervised learning of disentangled representations

Challenging common assumptions in the unsupervised learning of disentangled representations Locatello et al., ICML'19 Today’s paper choice won a best paper award at ICML’19. The ‘common assumptions’ that the paper challenges seem to be: "unsupervised learning of disentangled representations is possible, and useful!" The key idea behind the unsupervised learning of disentangled representations is that ... Continue Reading