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Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data.
Language models are few-shot learners
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Efficient scheduling of scientific workflows using hot metadata in a multisite cloud
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A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Bo Pang and Lillian Lee. 2004 · 2004
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Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
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Annotating expressions of opinions and emotions in language
Janyce Wiebe, Theresa Wilson, and Claire Cardie. 2005 · 2005
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The third PASCAL recognizing textual entailment challenge
Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007 · 2007
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Mime: Mimicking centralized stochastic algorithms in federated learning
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Cosine similarity scoring without score normalization techniques
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton. 2010 · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer. 2011 · 2011
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011 · 2011
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Efficient and customizable data partitioning framework for distributed big rdf data processing in the cloud
Kisung Lee, Ling Liu, Yuzhe Tang, Qi Zhang, and Yang Zhou. 2013 · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton. 2013 · 2013
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Residency aware inter-vm communication in virtualized cloud: Performance measurement and analysis
Qi Zhang, Ling Liu, Yi Ren, Kisung Lee, Yuzhe Tang, Xu Zhao, and Yang Zhou. 2013 · 2013
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Social influence based clustering of heterogeneous information networks
Yang Zhou and Ling Liu. 2013 · 2013
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Improving hadoop service provisioning in a geographically distributed cloud
Qi Zhang, Ling Liu, Kisung Lee, Yang Zhou, Aameek Singh, Nagapramod Mandagere, Sandeep Gopisetty, and Gabriel Alatorre. 2014 · 2014
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Policy-driven autonomic configuration management for nosql
Xianqiang Bao, Ling Liu, Nong Xiao, Yang Zhou, and Qi Zhang. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Scaling iterative graph computations with graphmap
Kisung Lee, Ling Liu, Karsten Schwan, Calton Pu, Qi Zhang, Yang Zhou, Emre Yigitoglu, and Pingpeng Yuan. 2015 · 2015
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A survey of data-intensive scientific workflow management
Ji Liu, Esther Pacitti, Patrick Valduriez, and Marta Mattoso. 2015 · 2015
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Graphlens: Mining enterprise storage workloads using graph analytics
Yang Zhou, Sangeetha Seshadri, Lawrence Chiu, and Ling Liu. 2014 · 2015
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Multi-objective scheduling of scientific workflows in multisite clouds
Ji Liu, Esther Pacitti, Patrick Valduriez, Daniel de Oliveira, and Marta Mattoso. 2016 · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017 · 2017
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Innovative Mining, Processing, and Application of Big Graphs
Yang Zhou. 2017 · 2017
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Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021 · 2021
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Neural architecture search without training
Joe Mellor, Jack Turner, Amos Storkey, and Elliot J Crowley. 2021 · 2021
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Local adaptivity in federated learning: Convergence and consistency
Jianyu Wang, Zheng Xu, Zachary Garrett, Zachary Charles, Luyang Liu, and Gauri Joshi. 2021 · 2021
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Topicka: Generating commonsense knowledge-aware dialogue responses towards the recommended topic fact
Sixing Wu, Ying Li, Dawei Zhang, Yang Zhou, and Zhonghai Wu. 2021 · 2021
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Federated composite optimization
Honglin Yuan, Manzil Zaheer, and Sashank Reddi. 2021 · 2021
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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BoolQ: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 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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Csm: A cloud service marketplace for complex service acquisition
Yexi Jiang, Chang-Shing Perng, Anca Sailer, Ignacio Silva-Lepe, Yang Zhou, and Tao Li. 2019 · 2019
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Lightwieight indexing and querying services for big spatial data
Kisung Lee, Ling Liu, Raghu L. Ganti, Mudhakar Srivatsa, Qi Zhang, Yang Zhou, and Qingyang Wang. 2019 · 2019
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Data-Intensive Workflow Management: For Clouds and Data-Intensive and Scalable Computing Environments
Daniel C. M. de Oliveira, Ji Liu, and Esther Pacitti. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Zeru Zhang, Jiayin Jin, Zijie Zhang, Yang Zhou, Xin Zhao, Jiaxiang Ren, Ji Liu, Lingfei Wu, Ruoming Jin, and Dejing Dou. 2021 · 2021
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Expressive 1-lipschitz neural networks for robust multiple graph learning against adversarial attacks
Xin Zhao, Zeru Zhang, Zijie Zhang, Lingfei Wu, Jiayin Jin, Yang Zhou, Ruoming Jin, Dejing Dou, and Da Yan. 2021 · 2021
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Attempt: Parameter-efficient multi-task tuning via attentional mixtures of soft prompts
Akari Asai, Mohammadreza Salehi, Matthew E Peters, and Hannaneh Hajishirzi. 2022 · 2022
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California consumer privacy act home page
Californians for Consumer Privacy. 2020 · 2022
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Federated fingerprint learning with heterogeneous architectures
Tianshi Che, Zijie Zhang, Yang Zhou, Xin Zhao, Ji Liu, Zhe Jiang, Da Yan, Ruoming Jin, and Dejing Dou. 2022 · 2022
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General data protection regulation
Official Journal of the European Union. 2016 · 2022
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M Rush. 2022 · 2022
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Guangyu Sun, Matias Mendieta, Taojiannan Yang, and Chen Chen. 2022 · 2022
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Fedbert: when federated learning meets pre-training
Yuanyishu Tian, Yao Wan, Lingjuan Lyu, Dezhong Yao, Hai Jin, and Lichao Sun. 2022 · 2022
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What language model architecture and pretraining objective works best for zero-shot generalization?
Thomas Wang, Adam Roberts, Daniel Hesslow, Teven Le Scao, Hyung Won Chung, Iz Beltagy, Julien Launay, and Colin Raffel. 2022 · 2022
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IDPG: An instance-dependent prompt generation method
Zhuofeng Wu, Sinong Wang, Jiatao Gu, Rui Hou, Yuxiao Dong, V.G.Vinod Vydiswaran, and Hao Ma. 2022 · 2022
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FedDUAP: Federated learning with dynamic update and adaptive pruning using shared data on the server
Hong Zhang, Ji Liu, Juncheng Jia, Yang Zhou, Huaiyu Dai, and Dejing Dou. 2022 · 2022
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Efficient device scheduling with multi-job federated learning
Chendi Zhou, Ji Liu, Juncheng Jia, Jingbo Zhou, Yang Zhou, Huaiyu Dai, and Dejing Dou. 2022 · 2022
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Fast federated machine unlearning with nonlinear functional theory
Tianshi Che, Yang Zhou, Zijie Zhang, Lingjuan Lyu, Ji Liu, Da Yan, Dejing Dou, and Jun Huan. 2023 · 2023
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Parameter-efficient fine-tuning of large-scale pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al. 2023 · 2023
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Ft-topo: Architecture-driven folded-triangle partitioning for communication-efficient graph processing
Xinbiao Gan, Guang Wu, Ruigeng Zeng, Jiaqi Si, Ji Liu, Daxiang Dong, Chunye Gong, Cong Liu, and Tiejun Li. 2023 · 2023
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Int. workshop on federated learning for distributed data mining
Junyuan Hong, Zhuangdi Zhu, Lingjuan Lyu, Yang Zhou, Vishnu Naresh Boddeti, and Jiayu Zhou. 2023 · 2023
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Fedhisyn: A hierarchical synchronous federated learning framework for resource and data heterogeneity
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LLama: Open and efficient foundation language models
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Fedprompt: Communication-efficient and privacy-preserving prompt tuning in federated learning
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