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
Author: adriancolyer
Venture Partner with Accel in London, author of 'The Morning Paper'
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
ExFaKT: a framework for explaining facts over knowledge graphs and text
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 ExFaKT: a framework for explaining facts over knowledge graphs and text
Graph neural networks: a review of methods and applications
Graph neural networks: a review of methods and applications Zhou et al., arXiv 2019 It’s another graph neural networks survey paper today! Cue the obligatory bus joke. Clearly, this covers much of the same territory as we looked at earlier in the week, but when we’re lucky enough to get two surveys published in short … Continue reading Graph neural networks: a review of methods and applications
A comprehensive survey on graph neural networks
A comprehensive survey on graph neural networks Wu et al., arXiv'19 Last year we looked at ‘Relational inductive biases, deep learning, and graph networks,’ where the authors made the case for deep learning with structured representations, which are naturally represented as graphs. Today’s paper choice provides us with a broad sweep of the graph neural … Continue reading A comprehensive survey on graph neural networks
the morning paper 