Bias Variance

تا امروز از کاربردهای متفاوت یادگیری عمیق در زمینه های گوناگون صحبت کرده ایم. به تازگی مقاله ای از دانشگاه British Columbia ارایه شده که استفاده یادگیری عمیق در علوم انسانی را به چالش کشیده است. در ادامه می خوانیم:

Deep Learning for Predicting Human Strategic Behavior

Predicting the behavior of human participants in strategic settings is an important problem in many domains. Most existing work either assumes that participants are perfectly rational, or attempts to directly model each participant’s cognitive processes based on insights from cognitive psychology and experimental economics. In this work, we present an alternative, a deep learning approach that automatically performs cognitive modeling without relying on such expert knowledge. We introduce a novel architecture that allows a single network to generalize across different input and output dimensions by using matrix units rather than scalar units, and show that its performance significantly outperforms that of the previous state of the art, which relies on expert-constructed features.


#یادگیری_عمیق #علوم_انسانی #معرفی_مقاله

🌳 پشتیبانی | 🌺 کانال | 🌴 سایت

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Bias Variance

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213 viewsBias Variance, edited  Nov 25, 2021 at 08:08

تا امروز از کاربردهای متفاوت یادگیری عمیق در زمینه های گوناگون صحبت کرده ایم. به تازگی مقاله ای از دانشگاه British Columbia ارایه شده که استفاده یادگیری عمیق در علوم انسانی را به چالش کشیده است. در ادامه می خوانیم:

Deep Learning for Predicting Human Strategic Behavior

Predicting the behavior of human participants in strategic settings is an important problem in many domains. Most existing work either assumes that participants are perfectly rational, or attempts to directly model each participant’s cognitive processes based on insights from cognitive psychology and experimental economics. In this work, we present an alternative, a deep learning approach that automatically performs cognitive modeling without relying on such expert knowledge. We introduce a novel architecture that allows a single network to generalize across different input and output dimensions by using matrix units rather than scalar units, and show that its performance significantly outperforms that of the previous state of the art, which relies on expert-constructed features.


#یادگیری_عمیق #علوم_انسانی #معرفی_مقاله

🌳 پشتیبانی | 🌺 کانال | 🌴 سایت

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