模型采矿工作坊

Shan You, Chang Xu, Fei Wang, Changshui Zhang
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引用次数: 1

摘要

如何挖掘预训练模型中的知识对于获得更有前景的性能具有重要意义,因为从业者可以很容易地访问许多预训练模型。本次模型挖掘研讨会旨在探讨在模型中挖掘知识的更多样化和更先进的方式,这种方式倾向于更明智、更优雅、更系统地利用预训练模型。与本次研讨会相关的主题有很多,例如通过师生范式从训练良好的重型模型中提炼出轻量级模型,并通过精心设计前代任务(如预训练、自监督和对比学习)来提高模型的性能。模型挖掘作为一种特殊的数据挖掘方式与SIGKDD相关,其受众包括研究人员和工程师将受益于为他们的任务设计更先进的算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Workshop on Model Mining
How to mine the knowledge in the pretrained models is of significance in achieving more promising performance, since practitioners have access to many pretrained models easily. This Workshop on Model Mining aims to investigate more diverse and advanced manners in mining knowledge within models, which tends to leverage the pretrained models more wisely, elegantly and systematically. There are many topics related to this workshop, such as distilling a lightweight model from a well-trained heavy model via teacher-student paradigm, and boosting the performance of the model by carefully designing the predecessor tasks, e.g., pre-training, self-supervised and contrastive learning. Model mining as a special way of data mining is relevant to SIGKDD, and its audience including researchers and engineers will benefit a lot for designing more advanced algorithms for their tasks.
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