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Earlier this year I had to install pytorch on a raspiberry pi for my robotic lawn mower project (more on that later). However, the process was very painful, so Ill throw my notes here in case anyone else tries to do the same. Its not supposed to be bullet-proof, but may help with some pointers. Updates to this proceudre may be found here. Installed from wheel on these: https://github.com/nmilosev/pytorch-arm-builds

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Papers: - Deisenroth, M. P., & Rasmussen, C. E. (2011). PILCO: A model-based and data-efficient approach to policy search. Proceedings of the 28th International Conference on Machine Learning, ICML 2011, 465–472. - Gal, Y., Mcallister, R. T., & Rasmussen, C. E. (2016). Improving PILCO with Bayesian Neural Network Dynamics Models. Data-Efficient Machine Learning Workshop, ICML, 1–7. The papers shows how to find good policies with relatively few observations on classical control problems (mountain car, pole swing up etc) using probabilistic model based reinforcement learning.

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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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