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Transferability estimation has been attached to great attention in the computer vision fields.
RoBERTa: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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EM algorithms for PCA and SPCA
Sam T. Roweis. 1997 · 1997
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A survey on transfer learning in natural language processing
Zaid Alyafeai, Maged Saeed AlShaibani, and Irfan Ahmad. 2020 · 2007
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Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks
Jason Phang, Thibault Févry, and Samuel R. Bowman. 2018 · 2018
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An information-theoretic approach to transferability in task transfer learning
Yajie Bao, Yang Li, Shao-Lun Huang, Lin Zhang, Lizhong Zheng, Amir Zamir, and Leonidas J. Guibas. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Representation similarity analysis for efficient task taxonomy & transfer learning
Kshitij Dwivedi and Gemma Roig. 2019 · 2019
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Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
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Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey E. Hinton. 2019 · 2019
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Transfer learning in natural language processing
Sebastian Ruder, Matthew E. Peters, Swabha Swayamdipta, and Thomas Wolf. 2019 · 2019
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Transferability and hardness of supervised classification tasks
Anh Tuan Tran, Cuong V. Nguyen, and Tal Hassner. 2019 · 2019
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 · 2019
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Taskonomy: Disentangling task transfer learning
Amir Zamir, Alexander Sax, William B. Shen, Leonidas J. Guibas, Jitendra Malik, and Silvio Savarese. 2019 · 2019
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PLATO: Pre-trained dialogue generation model with discrete latent variable
Siqi Bao, Huang He, Fan Wang, Hua Wu, and Haifeng Wang. 2020 · 2020
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Duality diagram similarity: A generic framework for initialization selection in task transfer learning
Kshitij Dwivedi, Jiahui Huang, Radoslaw Martin Cichy, and Gemma Roig. 2020 · 2020
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Source model selection for deep learning in the time series domain
Amiel Meiseles and Lior Rokach. 2020 · 2020
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A decade survey of transfer learning (2010-2020)
Shuteng Niu, Yongxin Liu, Jian Wang, and Houbing Song. 2020 · 2020
Cited alongside, same era.
Exploring and predicting transferability across NLP tasks
Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, and Mohit Iyyer. 2020 · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
Cited alongside, same era.
Scalable diverse model selection for accessible transfer learning
Daniel Bolya, Rohit Mittapalli, and Judy Hoffman. 2021 · 2021
Cited alongside, same era.
Graph-based similarity of neural network representations
Zuohui Chen, Yao Lu, Wen Yang, Qi Xuan, and Xiaoniu Yang. 2021 · 2021
Cited alongside, same era.
Frustratingly easy transferability estimation
Long-Kai Huang, Junzhou Huang, Yu Rong, Qiang Yang, and Ying Wei. 2022 · 2022
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Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance
Shibal Ibrahim, Natalia Ponomareva, and Rahul Mazumder. 2022 · 2022
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A review of deep transfer learning and recent advancements
Mohammadreza Iman, Khaled Rasheed, and Hamid R. Arabnia. 2022 · 2022
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Ensembling off-the-shelf models for GAN training
Nupur Kumari, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu. 2022 · 2022
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Cogtaskonomy: Cognitively inspired task taxonomy is beneficial to transfer learning in NLP
Yifei Luo, Minghui Xu, and Deyi Xiong. 2022 · 2022
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Exploiting a zoo of checkpoints for unseen tasks
Jiaji Huang, Qiang Qiu, and Kenneth Church. 2021 · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Cited alongside, same era.
Ranking neural checkpoints
Yandong Li, Xuhui Jia, Ruoxin Sang, Yukun Zhu, Bradley Green, Liqiang Wang, and Boqing Gong. 2021 · 2021
Cited alongside, same era.
Scalable transfer learning with expert models
Joan Puigcerver, Carlos Riquelme Ruiz, Basil Mustafa, Cédric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, and Neil Houlsby. 2021 · 2021
Cited alongside, same era.
Whitening sentence representations for better semantics and faster retrieval
Jianlin Su, Jiarun Cao, Weijie Liu, and Yangyiwen Ou. 2021 · 2021
Cited alongside, same era.
Logme: Practical assessment of pre-trained models for transfer learning
Kaichao You, Yong Liu, Jianmin Wang, and Mingsheng Long. 2021 · 2021
Cited alongside, same era.
Affective decoding for empathetic response generation
Chengkun Zheng, Guanyi Chen, Chenghua Lin, Ruizhe Li, and Zhi Chen. 2021 · 2021
Cited alongside, same era.
OpenAI. 2022 · 2022
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Transferability estimation using bhattacharyya class separability
Michal Pándy, Andrea Agostinelli, Jasper R. R. Uijlings, Vittorio Ferrari, and Thomas Mensink. 2022 · 2022
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Deep transfer learning for image classification: A survey
Jo Plested and Tom Gedeon. 2022 · 2022
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Not all models are equal: Predicting model transferability in a self-challenging fisher space
Wenqi Shao, Xun Zhao, Yixiao Ge, Zhaoyang Zhang, Lei Yang, Xiaogang Wang, Ying Shan, and Ping Luo. 2022 · 2022
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SPoT: Better frozen model adaptation through soft prompt transfer
Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou’, and Daniel Cer. 2022 · 2022
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Biomedical question answering: A survey of approaches and challenges
Qiao Jin, Zheng Yuan, Guangzhi Xiong, Qianlan Yu, Huaiyuan Ying, Chuanqi Tan, Mosha Chen, Songfang Huang, Xiaozhong Liu, and Sheng Yu. 2023 · 2023
Closest in time.
Are emergent abilities of large language models a mirage?
Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo. 2023 · 2023
Closest in time.
LLaMA: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Interactive natural language processing
Zekun Wang, Ge Zhang, Kexin Yang, Ning Shi, Wangchunshu Zhou, Shaochun Hao, Guangzheng Xiong, Yizhi Li, Mong Yuan Sim, Xiuying Chen, Qingqing Zhu, Zhenzhu Yang, Adam Nik, Qi Liu, Chenghua Lin, Shi Wang, Ruibo Liu, Wenhu Chen, Ke Xu, Dayiheng Liu, Yike Guo, and Jie Fu. 2023 · 2023
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GLM-130B: An open bilingual pre-trained model
Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, Wenguang Chen, Zhiyuan Liu, Peng Zhang, Yuxiao Dong, and Jie Tang. 2023 · 2023
Closest in time.
Evaluating open-domain dialogues in latent space with next sentence prediction and mutual information
Kun Zhao, Bohao Yang, Chenghua Lin, Wenge Rong, Aline Villavicencio, and Xiaohui Cui. 2023 · 2023
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