Month: February 2019

GAN dissection: visualizing and understanding generative adversarial networks

~ Adrian Colyer ~ 2 Comments
GAN dissection: visualizing and understanding generative adversarial networks Bau et al., arXiv'18 Earlier this week we looked at visualisations to aid understanding and interpretation of RNNs, today’s paper choice gives us a fascinating look at what happens inside a GAN (generative adversarial network). In addition to the paper, the code is available on GitHub and ... Continue Reading

CORALS: who are my potential new customers? Tapping into the wisdom of customers’ decisions

~ Adrian Colyer ~ 3 Comments
CORALS: who are my potential new customers? Tapping into the wisdom of customers' decisions Li et al., WSDM'19 The authors of this paper won round 9 of the Yelp dataset challenge for their work. The goal is to find new target customers for local businesses by mining location-based checkins of users, user preferences, and online ... Continue Reading

Protecting user privacy: an approach for untraceable web browsing history and unambiguous user profiles

~ Adrian Colyer ~ 2 Comments
Protecting user privacy: an approach for untraceable web browsing history and unambiguous user profiles Beigi et al., WSDM'19 Maybe you’re reading this post online at The Morning Paper, and you came here by clicking a link in your Twitter feed because you follow my paper write-up announcements there. It might even be that you fairly ... Continue Reading

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

ExFaKT: a framework for explaining facts over knowledge graphs and text

~ Adrian Colyer ~ 6 Comments
ExFaKT: a framework for explaining facts over knowledge graphs and text Gad-Elrab et al., WSDM'19 Last week we took a look at Graph Neural Networks for learning with structured representations. Another kind of graph of interest for learning and inference is the knowledge graph. Knowledge Graphs (KGs) are large collections of factual triples of the ... Continue Reading