Optimal Brain Damage 期刊:NIPS 1990 针对场景、问题: 神经网络学习在现实世界问题上的大多数成功应用都是使用规模相当大的高度结构化网络实现的。随着应用程序变得越来越复杂,网络可能会变得更加庞大和结构化。需要设计工具和技术来比较不同的体系结构和最小化网络规模。更重要的是,随着系统中参数数量的增加,可能会出...
GPTQ运用海塞矩阵找下一个量化权重,论文中没有相关证明。因此翻阅OBD和OBS论文,OBD是这篇Yann Le Cun 1989年的论文(彼时还在贝尔实验室)。总体感觉上下文说明比较多,比较容易读懂。 Optimal Brain Damage,可顾名思义,最优大脑损坏,大脑神经元如果要选择损坏一部分权重,应该去掉哪些?就是经典的神经元剪枝问题。剪枝...
optimal brain damage 翻译 The translation of "optimal brain damage" is "最优脑损伤" (zuì yōu nǎo sǔn shāng) in Chinese. "Optimal brain damage" refers to a technique used in neural network pruning, where unnecessary connections or parameters in a neural network are identified and ...
OptimalBrainDamage-UniversityofMinnesotaDuluth 系统标签: brainoptimalduluthdamageminnesotaobd OptimalBrainDamage YannLeCun,JohnS.DenkerandSaraA.Solla presentedby ChaitanyaPolumetla Overview • Introduction • NeedforOBD • TheIdea • AuthorsProposal • WhyOBDcouldwork? • Experiments • Results ...
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Optimal Brain Damage Yann Le Cun, John S. Denker and Sara A. Solla AT&T Bell Laboratories, Holmdel, N. J. 07733 Most successful applications of neural network learning to real-world problems have been achieved using highly structured networks of rather large size for example (Waibel, 1989; ...
Optimal Brain Damage (OBD) and Optimal Brain Surgeon (OBS) represent two popular pruning procedures; however, pruning large networks trained on voluminous ... TOLSTRUP,N. - 《International Journal of Neural Systems》 被引量: 17发表: 1995年 Modelling of oil agglomeration of dolomite by the use ...
Winther, "A quantitative study of pruning by optimal brain damage", Int. J. Neural Syst. , vol. 4, no. 2, pp. 159-169, 1993J. Gorodkin, L. K. Hansen, A. Krogh, C. Svarer and O. Winther 1993, "A quantitative study of pruning by optimal brain damage," IJNS 4, 159-169....
ResearchGate链接:https://www.researchgate.net/publication/3568764_Optimal_Brain_Surgeon_and_general_network_pruning OBS表示它不只做damage,还会做外科手术,它把一个权重调到0,会顺带把其他权重做自动调整,从而也无需做增量重训。 回到OBD的论文中的方程(1): ...
Optimal Brain Damage (OBD) and Optimal Brain Surgeon (OBS) represent two popular pruning procedures; however, pruning large networks trained on voluminous data sets using these methods easily becomes intractable. We present a number of approximations and discuss practical issues in real-world pruning,...