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
Month: February 2019
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
