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1	Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation ...
	The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Zhang, Bo. - : Underline Science Inc., 2021
	Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.421 Abstract: Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge of source domain to the unlabeled target domain. Existing methods typically require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains. However, this pipeline makes the source data risky and is inflexible for deploying the target model. This paper tackles a novel setting where only a trained source model is available and different network architectures can be adapted for target domain in terms of deployment environments. We propose a generic framework named \emph{Cross-domain Knowledge Distillation} (CdKD) without needing any source data. CdKD matches the joint distributions between a trained source model and a set of target data during distilling the knowledge from the source model to the target domain. As a type of important knowledge in the source domain, for the first time, the gradient information is ...
	Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
	URL: https://dx.doi.org/10.48448/z0jg-5x37 https://underline.io/lecture/25847-matching-distributions-between-model-and-data-cross-domain-knowledge-distillation-for-unsupervised-domain-adaptation
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2	List-wise learning to rank biomedical question-answer pairs with deep ranking recursive autoencoders
	Yan, Yan; Zhang, Bo-Wen; Li, Xu-Feng...
	In: PLoS One (2020)
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3	Dimensionality reduction: An interpretation from manifold regularization perspective
	Fan, Mingyu; Gu, Nannan; Qiao, Hong...
	In: Information sciences. - New York, NY : Elsevier Science Inc. 277 (2014), 694-714
	OLC Linguistik
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4	Classification of L2 vocabulary learning strategies: evidence from exploratory and confirmatory factor analyses
	Zhang, Bo; Li, Changyu
	In: Regional Language Centre <Singapur>. RELC journal. - London : Sage 42 (2011) 2, 141-154
	BLLDB
	OLC Linguistik
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5	Assessing the accuracy and consistency of language proficiency classification under competing measurement models
	Zhang, Bo
	In: Language testing. - London : Sage 27 (2010) 1, 119-140
	BLLDB
	OLC Linguistik
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6	Fuzzy reasoning model under quotient space structure
	Zhang, Ling; Zhang, Bo
	In: Information sciences. - New York, NY : Elsevier Science Inc. 173 (2005) 4, 353
	OLC Linguistik
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7	SIMILARITY MEASURE
	Zhang Bo; Peng Gang; Wang William S-y
	In: http://www.colips.org/conference/iscslp2006/anthology/2000/paper/PSB1/060.pdf
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8	ROBUST SPEECH END-POINT DETECTION AND F0 EXTRACTION BY APPLYING IMAGE PROCESSING TECHNIQUES 1
	Zhang Bo; Peng Gang
	In: http://isca-speech.org/archive_open/archive_papers/iscslp1998/SCA6.pdf
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9	TEACHING EFFICIENT READING IN A COLLEGE ENGLISH CLASSROOM
	Zhang Bo
	In: http://www.celea.org.cn/teic/67/67-82.pdf
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