Bias Variance

چند روز پیش در آرکایو مقاله ای مروری قرار گرفت که در حوزه green deep learning است. در ادامه می خوانیم:

A Survey on Green Deep Learning

In recent years, larger and deeper models are springing up and continuously pushing state-of-the-art (SOTA) results across various fields like natural language processing (NLP) and computer vision (CV). However, despite promising results, it needs to be noted that the computations required by SOTA models have been increased at an exponential rate. Massive computations not only have a surprisingly large carbon footprint but also have negative effects on research inclusiveness and deployment on real-world applications. Green deep learning is an increasingly hot research field that appeals to researchers to pay attention to energy usage and carbon emission during model training and inference. The target is to yield novel results with lightweight and efficient technologies. Many technologies can be used to achieve this goal, like model compression and knowledge distillation. This paper focuses on presenting a systematic review of the development of Green deep learning technologies. We classify these approaches into four categories: (1) compact networks, (2) energy-efficient training strategies, (3) energy-efficient inference approaches, and (4) efficient data usage. For each category, we discuss the progress that has been achieved and the unresolved challenges.

پیشنهاد می کنیم اگر با این زمینه آشنا نیستید، حتما فهرست مطالب را ببینید. این زمینه می تواند به عنوان حوزه ای برای انجام پایان نامه انتخاب شود.


#مقاله_مروری #معرفی_مقاله #green_deep_learning #یادگیری_عمیق

🌳 پشتیبانی | 🌺 کانال | 🌴 سایت

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Bias Variance

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210 viewsBias Variance, edited  Nov 19, 2021 at 07:34

چند روز پیش در آرکایو مقاله ای مروری قرار گرفت که در حوزه green deep learning است. در ادامه می خوانیم:

A Survey on Green Deep Learning

In recent years, larger and deeper models are springing up and continuously pushing state-of-the-art (SOTA) results across various fields like natural language processing (NLP) and computer vision (CV). However, despite promising results, it needs to be noted that the computations required by SOTA models have been increased at an exponential rate. Massive computations not only have a surprisingly large carbon footprint but also have negative effects on research inclusiveness and deployment on real-world applications. Green deep learning is an increasingly hot research field that appeals to researchers to pay attention to energy usage and carbon emission during model training and inference. The target is to yield novel results with lightweight and efficient technologies. Many technologies can be used to achieve this goal, like model compression and knowledge distillation. This paper focuses on presenting a systematic review of the development of Green deep learning technologies. We classify these approaches into four categories: (1) compact networks, (2) energy-efficient training strategies, (3) energy-efficient inference approaches, and (4) efficient data usage. For each category, we discuss the progress that has been achieved and the unresolved challenges.

پیشنهاد می کنیم اگر با این زمینه آشنا نیستید، حتما فهرست مطالب را ببینید. این زمینه می تواند به عنوان حوزه ای برای انجام پایان نامه انتخاب شود.


#مقاله_مروری #معرفی_مقاله #green_deep_learning #یادگیری_عمیق

🌳 پشتیبانی | 🌺 کانال | 🌴 سایت

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