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Slides from my presentation on “Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo” by Salakhutdinov and Mnih. Paper link. The slides can be downloaded here.

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For the first time Ive actually made a summary of all the papers and presentations I found noteworthy at a conference (allright, there were more, but this is a start). Below is my notes, with links etc. The purpose of the notes is mainly for myself to remember and revisit what I found interesting, but I see no reasons not to share to others. Does not include my own paper.

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Ning Zhou, Audun Øygard and I got a paper in the KDD workshop Deep Learning Day. We provide some practitioner’s findings on applying deep learning recommendations in production! Link to paper here. Together with @nzhou9 and @matsiyatzy, I am officially moving into academia after being an industrial observer: We got a paper in the #KDD2018 workshop Deep Learning Day. We provide some practitioner's findings on applying deep learning recommendations in production!

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I am not a fan of new years resolutions. If I had been, one of my new years resolutions would be to be better at writing down what I am doing all the time. However, I held some talks and did some fun experiments in 2017, so the cheap way is simply to link to those. How to become a Data Scientist in 20 minutes (JavaZone 2017) At Javazone 2017 I held a short talk on how building (drum roll) machine learning algorithms is actually pretty easy.

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During a hackathon at FINN.no, we figured we wanted to learn more about deep NLP-models. FINN.no has a large database with ads of people trying to sell stuff (around 1 million active ads at any time), and they are categorized into a category tree with three or four layers. For example, full suspension bikes can be found under “Sport and outdoor activities” / “Bike sport” / “Full suspension bikes”.

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