Issue #152
July 4, 2021
- 1. Mathematical Foundations of Monte Carlo Methods [scratchapixel.com]
- 2. The Braess Paradox [rjlipton.wpcomstaging.com]
- 3. What Geometric Deep Learning is all about? [a-j.gitbook.io]
- 4. There’s more to mathematics than rigour and proofs [terrytao.wordpress.com]
- 5. The Curse of Systems Thinkers [blog.relyabilit.ie]
- 6. It Took Me 10 Years to Understand Entropy, Here is What I Learned [cantorsparadise.com]
- 7. Backtesting Machine Learning Models the Uber Way [pub.towardsai.net]
- • Reconciling modern machine learning practice and the bias-variance trade-off (M. Belkin, D. Hsu, S. Ma, S. Mandal)
- • Degree correlations in graphs with clique clustering (P. Mann, V. A. Smith, J. B. O. Mitchell, S. Dobson)
- • Do Human Mobility Network Analyses Produced from Different Location-based Data Sources Yield Similar Results across Scales? (C.-W. Hsu, C. Liu, K. M. Nguyen, Y.-H. Chien, A. Mostafavi)
- • Fairness in Graph Mining: A Survey (Y. Dong, J. Ma, C. Chen, J. Li)
- • How rarity shapes the NFT market (A. Mekacher, A. Bracci, M. Nadini, M. Martino, L. Alessandretti, L. M. Aiello, A. Baronchelli)
- • Planting Undetectable Backdoors in Machine Learning Models (S. Goldwasser, M. P. Kim, V. Vaikuntanathan, O. Zamir)
A New Golden Age for Computer Architecture: History, Challenges and Opportunities
All our videos are also available in our YouTube playlist.
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