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31#
發(fā)表于 2025-3-26 21:01:47 | 只看該作者
Hazel Rymer,María Martínez,Jorge Brenes,Glyn Williams-Jones,Andrea Borgiaorithm that uses a Bayesian approach to collect only the amount of data necessary to reach a specified confidence level in the BCI’s decision based on continuous evaluation of the quality of a user’s responses. We further optimized the algorithm by incorporating statistical information about the use
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發(fā)表于 2025-3-27 01:31:45 | 只看該作者
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發(fā)表于 2025-3-27 06:50:06 | 只看該作者
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發(fā)表于 2025-3-27 09:57:03 | 只看該作者
Keeping it Low-Key: Modern-Day Approaches to Privacy-Preserving Machine Learning,g lists to who we connect with or follow on a social network. Additionally, the COVID-19 pandemic has accelerated the growth and consumption of these digital services across the globe. Regardless of our fascination or loathe for it, ML-powered services and products are influencing our decision-makin
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發(fā)表于 2025-3-27 14:01:22 | 只看該作者
36#
發(fā)表于 2025-3-27 19:43:35 | 只看該作者
https://doi.org/10.1007/978-1-4757-1887-4e third section summarizes the chapter authors’ reflections on principles of good experimentation and the possibilities to adhere to these principles in practical work. Finally, we point to a few challenges ahead in the further development of experimental political science.
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