Issue #61
July 26, 2020
- 1. Deep learning to translate between programming languages [ai.facebook.com]
- 2. Using causal graphs to understand missingness and how to deal with it [jakewestfall.org]
- 3. H3: Uber's Hexagonal Hierarchical Spatial Index [eng.uber.com]
- 4. NumPy Fundamentals for Data Science and Machine Learning [pabloinsente.github.io]
- 5. Causal Reinforcement Learning [crl.causalai.net]
- 6. AI in physics: are we facing a scientific revolution? [4alltech.com]
- 7. How a Kalman filter works, in pictures [bzarg.com]
- 8. Machine Learning Research at Apple [machinelearning.apple.com]
- 9. Why doesn't Python have a main function? [towardsdatascience.com]
- 10. (Very) Basic Intro To Elliptic Curve Cryptography [qvault.io]
- • A curated collection of COVID-19 online datasets (I. Inuwa-Dutse, I. Korkontzelos)
- • Universal inference (L. Wasserman, A. Ramdas, S. Balakrishnan)
- • Mathematical models to guide pandemic response (C. J. E. Metcalf, D. H. Morris, S. W. Park)
- • Discovery of SARS-CoV-2 antiviral drugs through large-scale compound repurposing (Laura Riva, Shuofeng Yuan, Sumit K. Chanda)
- • Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data (C. Brown, J. Chauhan, A. Grammenos, J. Han, A. Hasthanasombat, D. Spathis, T. Xia, P. Cicuta, C. Mascolo)
- • Google Books Ngram: Problems of Representativeness and Data Reliability (V. D. Solovyev, V. V. Bochkarev, S. S. Akhtyamova)
- • Data Stream Clustering: A Review (A. Zubaroğlu, V. Atalay)
- • A Mathematical Theory of Attention (J. Vuckovic, A. Baratin, R. T. des Combes)
- • Initialization of a Disease Transmission Model (H. Runvik, A. Medvedev, R. Eriksson, S. Engblom)
- • Graph-based process mining (A. Jalali)
Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI | AI Podcast #74
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