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The scaling law is a notable property of neural network models and has significantly propelled the development of large language models.
Psychological review , 65(6):386, 1958
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Multilayer feedforward networks are universal approximators
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Global optimization by basin-hopping and the lowest energy structures of lennard-jones clusters containing up to 110 atoms
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Efficient cost-aware cascade ranking in multi-stage retrieval
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Deep learning scaling is predictable, empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
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Attention is all you need
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The lambdaloss framework for ranking metric optimization
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Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai · 2018
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Stochastic optimization of sorting networks via continuous relaxations
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Pytorch: An imperative style, high-performance deep learning library
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Pairwise ranking losses of click-through rates prediction for welfare maximization in ad auctions
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Recommender systems with generative retrieval
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Language models are few-shot learners
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Scaling laws for neural language models
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