8 Matching Annotations
  1. Last 7 days
    1. benchmarks sourced from publicly available material carry contamination risk, where training-data exposure can silently inflate scores.

      大多数人认为公开数据集是AI评估的金标准,能够提供客观公正的测试环境。但作者警告,使用公开材料构建的基准测试存在污染风险,训练数据接触会悄无声息地提高分数。这一观点挑战了AI评估领域的传统做法,暗示我们需要更严格的数据隔离措施或转向私有数据集进行评估。

  2. Sep 2021
    1. Haber, N. A., Wieten, S. E., Rohrer, J. M., Arah, O. A., Tennant, P. W. G., Stuart, E. A., Murray, E. J., Pilleron, S., Lam, S. T., Riederer, E., Howcutt, S. J., Simmons, A. E., Leyrat, C., Schoenegger, P., Booman, A., Dufour, M.-S. K., O’Donoghue, A. L., Baglini, R., Do, S., … Fox, M. P. (2021). Causal and Associational Linking Language From Observational Research and Health Evaluation Literature in Practice: A systematic language evaluation [Preprint]. Epidemiology. https://doi.org/10.1101/2021.08.25.21262631

  3. Sep 2020
  4. Jul 2020
  5. Apr 2020
  6. Mar 2019
    1. This link is to a three-page PDF that describes Gagne's nine events of instruction, largely in in the form of a graphic. Text is minimized and descriptive text is color coded so it is easy to find underneath the graphic at the top. The layout is simple and easy to follow. A general description of Gagne's work is not part of this page. While this particular presentation does not have personal appeal to me, it is included here due to the quality of the page and because the presentation is more user friendly than most. Rating 4/5

    1. This is a description of the form of backward design referred to as Understanding by Design. In its simplest form, this is a three step process in which instructional designers first specify desired outcomes and acceptable evidence before specifying learning activities. This presentation may be a little boring to read as it is text-heavy and black and white, but those same attributes make it printer friendly. rating 3/5