Issue #4

June 23, 2019

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1. 1. A Practical Guide To Building Recommender Systems [buildingrecommenders.wordpress.com]
2. 2. Self-Supervised Learning Lecture Notes [inria.fr]
3. 3. BIG, small or Right Data: Which is the proper focus? [www.kdnuggets.com]
4. 4. Getting to Know Python 3.7: Data Classes, async/await and More! [blog.heroku.com]
5. 5. A drawing bot for realizing everyday scenes—and even stories [microsoft.com]
6. 6. Introduction to Graphs (Part 1) [towardsdatascience.com]
7. 7. The Coming AI Autumn [jeffreybigham.com]

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• • Neural Architecture Search with Reinforcement Learning (B. Zoph, Q. V. Le)
• • The scientific case for brain simulations (G. T. Einevoll, A. Destexhe, M. Diesmann, S. Grün, V. Jirsa, M. de Kamps, M. Migliore, T. V. Ness, H. E. Plesser, F. Schürmann)
• • Recurrent Neural Processes (T. Willi, J. Masci, J. Schmidhuber, C. Osendorfer)
• • TensorNetwork for Machine Learning (S. Efthymiou, J. Hidary, S. Leichenauer)
• • Network stiffness: A new topological property in complex networks (D. Tsiotas)
• • A/B Testing Measurement Framework for Recommendation Models Based on Expected Revenue (M. Hejazinia, M. Hosseini, B. Sih)

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