Rezultati pretraživanja
  1. prije 24 sata

    "The measure and mismeasure of fairness: a critical review of fair machine learning" Corbett-Davies & Goel, If we want fair machine learning models, then first we're going to need a working definition of 'fair'...

  2. 2. velj
    Odgovor korisnicima

    I'd submit and are a great starting points of this :) Papers We Love has grown as well, mushrooming into a nice metacommunity with multiple chapters across multiple cities.

  3. Today, covers "Seamless offloading of web app computations from mobile device to edge clouds via HTML5 Web Worker migration" from ACM SoCC'19:

  4. 31. sij

    "Seamless offloading of web app computations from mobile device to edge clouds via HTML5 web worker migration" Jeong et al., Web workers let you offload computation to another thread... and now to the edge too!

  5. 29. sij

    "Narrowing the gap between serverless and its state with storage functions" Zhang et al., 'Stored Functions' for serverless workload data-processing efficiency

  6. Yesterday, covered "Reverb: Speculative Debugging for Web Applications," from ACM SOCC’19:

  7. 27. sij

    Reverb: speculative debugging for web applications A tool that can record your js, then let you find a bug and fix it and _test it_ locally on those logs as they replay before you ship out the change. Some at blog.

  8. 27. sij

    "Reverb: speculative debugging for web applications" Netravali & Mickens, Best paper award winner at

  9. 24. sij

    "Trade-offs under pressure: heuristics and observations of teams resolving internet service outages" Allspaw, (Part 2) The greatest sources of success in automation-rich environments are people.

  10. 22. sij

    "Trade-offs under pressure: heuristics and observations of teams resolving internet service outages" Allspaw, (Part 1) A foundation for reasoning about the way teams of operators resolve incidents.

  11. 20. sij

    "STELLA: report from the SNAFU-catchers workshop on coping with complexity" Woods 2017, Coping with the complexity of modern systems and incident management.

  12. 19. sij

    RT "Programmatically interpretable reinforcement learning" Verma et al., RL policies that are human interpretable and verifiable - i.e., deployable!

  13. 18. sij

    Synthesizing data structure transformations from input-output examples via

  14. 17. sij

    "Synthesizing data structure transformations from input-output examples" Feser et al., The 'no-code' approach to data transforms.

  15. 15. sij

    "Programmatically interpretable reinforcement learning" Verma et al., RL policies that are human interpretable and verifiable - i.e., deployable!

  16. 13. sij

    "Challenges of real-world reinforcement learning" Dulac-Arnold et al., Is reinforcement learning ready for the real-world?

  17. 10. sij

    "Ten challenges for making automation a 'team player' in joint human-agent activity" Klein et al., What does it take to build effective human-computer collaborations?

  18. 8. sij
  19. 8. sij

    "Ironies of automation" Bainbridge, The more we automate, the more dependent we become on highly-skilled human operators!

  20. 6. sij

    Here comes another year of ! What will *you* be reading this year? .

Čini se da učitavanje traje već neko vrijeme.

Twitter je možda preopterećen ili ima kratkotrajnih poteškoća u radu. Pokušajte ponovno ili potražite dodatne informacije u odjeljku Status Twittera.