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Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton · 2002
Earlier work this paper cites.
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Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, and Ahmed Awadallah · 2023
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Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2023
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InheritSumm: A general, versatile and compact summarizer by distilling from GPT
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Mammoth: Building math generalist models through hybrid instruction tuning, 2023
Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen · 2023
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