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Yaqing Wang
Researcher at Baidu
Publications - 34
Citations - 2107
Yaqing Wang is an academic researcher from Baidu. The author has contributed to research in topics: Computer science & Low-rank approximation. The author has an hindex of 7, co-authored 21 publications receiving 686 citations. Previous affiliations of Yaqing Wang include Hong Kong University of Science and Technology.
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Generalizing from a Few Examples: A Survey on Few-shot Learning
TL;DR: A thorough survey to fully understand Few-shot Learning (FSL), and categorizes FSL methods from three perspectives: data, which uses prior knowledge to augment the supervised experience; model, which used to reduce the size of the hypothesis space; and algorithm, which using prior knowledgeto alter the search for the best hypothesis in the given hypothesis space.
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Generalizing from a Few Examples: A Survey on Few-Shot Learning
TL;DR: A thorough survey to fully understand Few-Shot Learning (FSL), and categorizes FSL methods from three perspectives: data, which uses prior knowledge to augment the supervised experience; model, which used to reduce the size of the hypothesis space; and algorithm, which using prior knowledgeto alter the search for the best hypothesis in the given hypothesis space.
Journal ArticleDOI
Scalable Online Convolutional Sparse Coding.
TL;DR: This paper reformulation of the CSC objective so that convolution can be handled easily in the frequency domain, and much smaller history matrices are needed, and uses the alternating direction method of multipliers (ADMMs) to solve the resultant optimization problem.
Journal ArticleDOI
AdaMix: Mixture-of-Adapter for Parameter-efficient Tuning of Large Language Models
TL;DR: This work introduces a new mechanism to improve adapter capacity without increasing parameters or computational cost by two key techniques and demonstrates these techniques to work well across multiple task settings including fully supervised and few-shot Natural Language Understanding tasks.
Journal ArticleDOI
Scalable Online Convolutional Sparse Coding
TL;DR: In this paper, the authors proposed a reformulation of the CSC objective so that convolution can be handled easily in the frequency domain and much smaller history matrices are needed.