GAN dissection: visualizing and understanding generative adversarial networks

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 GAN dissection: visualizing and understanding generative adversarial networks

Understanding hidden memories of recurrent neural networks

Understanding hidden memories of recurrent neural networks Ming et al., VAST’17 Last week we looked at CORALS, winner of round 9 of the Yelp dataset challenge. Today’s paper choice was a winner in round 10. We’re used to visualisations of CNNs, which give interpretations of what is being learned in the hidden layers. But the … Continue reading Understanding hidden memories of recurrent neural networks

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

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

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

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

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

A survey on dynamic and stochastic vehicle routing problems

A survey on dynamic and stochastic vehicle routing problems Ritzinger et al., International Journal of Production Research It’s been a while since we last looked at an overview of dynamic vehicle routing problems: that was back in 2014 (See ‘Dynamic vehicle routing, pickup, and delivery problems’). That paper has fond memories for me, I looked … Continue reading A survey on dynamic and stochastic vehicle routing problems

Beyond news contents: the role of social context for fake news detection

Beyond news contents: the role of social context for fake news detection Shu et al., WSDM'19 Today we’re looking at a more general fake news problem: detecting fake news that is being spread on a social network. Forgetting the computer science angle for a minute, it seems intuitive to me that some important factors here … Continue reading Beyond news contents: the role of social context for fake news detection