Issue #97
April 4, 2021
- 1. Major flaws found in machine learning for COVID-19 diagnosis [venturebeat.com]
- 2. Stop Calling Everything AI, Machine-Learning Pioneer Says [spectrum.ieee.org]
- 3. Why machine learning struggles with causality [bdtechtalks.com]
- 4. Building Smarter Search Products: 3 Steps for Evaluating Search Algorithms [shopify.engineering]
- 5. The Mathematical Pranksters behind Nicolas Bourbaki [daily.jstor.org]
- 6. Common Crawl dataset [commoncrawl.org]
- 7. Yoshua Bengio Team Proposes Causal Learning to Solve the ML Model Generalization Problem [medium.com/syncedreview]
- 8. What is Causal Data Fusion? [causalscience.org]
- • Universal resilience patterns in labor markets (E. Moro, M. R. Frank, A. Pentland, A. Rutherford, M. Cebrian, I. Rahwan)
- • "Everyone wants to do the model work, not the data work": Data Cascades in High-Stakes AI (N. Sambasivan, S. Kapania, H. Highfill, D. Akrong, P. K. Paritosh, L. M. Aroyo)
- • Why is AI hard and Physics simple? (D. A. Roberts)
- • A Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learning (R. Zhang, B. D. Trisedy, M. Li, Y. Jiang, J. Qi)
- • Learning advanced mathematical computations from examples (F. Charton, A. Hayat, G. Lample)
- • Improved Small-Sample Estimation of Nonlinear Cross-Validated Prediction Metrics (D. Benkeser, M. Petersen, M. J. van der Laan)
- • Persona2vec: a flexible multi-role representations learning framework for graphs (J. Yoon, K.-C. Yang, W.-S. Jung, Y.-Y. Ahn)
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