Fetching the paper…
Reading the bibliography…
As sufficient data are not always publically accessible for model training, researchers exploit limited data with advanced learning algorithms or expand the dataset via data augmentation (DA).
Eda: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou. 2019 · 1901
Earlier work this paper cites.
Clinicalbert: Modeling clinical notes and predicting hospital readmission
Kexin Huang, Jaan Altosaar, and Rajesh Ranganath. 2019 · 1904
Earlier work this paper cites.
Jinshuo Dong, Aaron Roth, and Weijie J Su. 2019 · 1905
Earlier work this paper cites.
Augmenting data with mixup for sentence classification: An empirical study
Hongyu Guo, Yongyi Mao, and Richong Zhang. 2019 · 1905
Earlier work this paper cites.
Health insurance portability and accountability act of 1996
Accountability Act. 1996 · 1996
Earlier work this paper cites.
Data augmentation using pre-trained transformer models
Varun Kumar, Ashutosh Choudhary, and Eunah Cho. 2020 · 2003
Earlier work this paper cites.
Bert-attack: Adversarial attack against bert using bert
Linyang Li, Ruotian Ma, Qipeng Guo, Xiangyang Xue, and Xipeng Qiu. 2020 · 2004
Earlier work this paper cites.
Generative data augmentation for commonsense reasoning
Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, and Doug Downey. 2020 · 2004
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006 · 2006
Earlier work this paper cites.
One-shot learning of object categories
Li Fei-Fei, Robert Fergus, and Pietro Perona. 2006 · 2006
Earlier work this paper cites.
Learning in a large function space: Privacy-preserving mechanisms for svm learning
Benjamin IP Rubinstein, Peter L Bartlett, Ling Huang, and Nina Taft. 2009 · 2009
Earlier work this paper cites.
Local additivity based data augmentation for semi-supervised ner
Jiaao Chen, Zhenghui Wang, Ran Tian, Zichao Yang, and Diyi Yang. 2020 · 2010
Earlier work this paper cites.
Seqmix: Augmenting active sequence labeling via sequence mixup
Rongzhi Zhang, Yue Yu, and Chao Zhang. 2020 · 2010
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate. 2011 · 2011
Earlier work this paper cites.
Privacy-preserving data exploration in genome-wide association studies
Aaron Johnson and Vitaly Shmatikov. 2013 · 2013
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate. 2013 · 2013
Earlier work this paper cites.
Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
De-identification of Personal Information:
Simson Garfinkel et al. 2015 · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Earlier work this paper cites.
Fast text anonymization using k-anonyminity
Wakana Maeda, Yu Suzuki, and Satoshi Nakamura. 2016 · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016 · 2016
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Cited alongside, same era.
Learning active learning from data
Ksenia Konyushkova, Raphael Sznitman, and Pascal Fua. 2017 · 2017
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017 · 2017
Large-scale differentially private bert
Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, and Pasin Manurangsi. 2021 · 2021
Later among the works it cites.
On the convergence of deep learning with differential privacy
Zhiqi Bu, Hua Wang, Qi Long, and Weijie J Su. 2021 · 2021
Later among the works it cites.
Uncertainty-aware self-training for semi-supervised event temporal relation extraction
Pengfei Cao, Xinyu Zuo, Yubo Chen, Kang Liu, Jun Zhao, and Wei Bi. 2021 · 2021
Later among the works it cites.
Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al. 2021 · 2021
Later among the works it cites.
Aeda: an easier data augmentation technique for text classification
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martın Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar. 2017 · 2017
Cited alongside, same era.
A k-anonymized text generation method
Yu Suzuki, Koichiro Yoshino, and Satoshi Nakamura. 2018 · 2017
Cited alongside, same era.
Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Borja Balle and Yu-Xiang Wang. 2018 · 2018
Cited alongside, same era.
Contextual augmentation: Data augmentation by words with paradigmatic relations
Sosuke Kobayashi. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Learning data manipulation for augmentation and weighting
Zhiting Hu, Bowen Tan, Russ R Salakhutdinov, Tom M Mitchell, and Eric P Xing. 2019 · 2019
Cited alongside, same era.
Akbar Karimi, Leonardo Rossi, and Andrea Prati. 2021 · 2021
Later among the works it cites.
A hybrid medical text classification framework: Integrating attentive rule construction and neural network
Xiang Li, Menglin Cui, Jingpeng Li, Ruibin Bai, Zheng Lu, and Uwe Aickelin. 2021 · 2021
Later among the works it cites.
Selective differential privacy for language modeling
Weiyan Shi, Aiqi Cui, Evan Li, Ruoxi Jia, and Zhou Yu. 2021 · 2021
Later among the works it cites.
A secure and efficient federated learning framework for nlp
Jieren Deng, Chenghong Wang, Xianrui Meng, Yijue Wang, Ji Li, Sheng Lin, Shuo Han, Fei Miao, Sanguthevar Rajasekaran, and Caiwen Ding. 2022 · 2022
Later among the works it cites.
Exploring the limits of differentially private deep learning with group-wise clipping
Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, and Jiang Bian. 2022 · 2022
Later among the works it cites.
Large language models can be strong differentially private learners
Xuechen Li, Florian Tramèr, Percy Liang, and Tatsunori Hashimoto. 2022 · 2022
Later among the works it cites.
Seqpate: Differentially private text generation via knowledge distillation
Zhiliang Tian, Yingxiu Zhao, Ziyue Huang, Yu-Xiang Wang, Nevin L Zhang, and He He. 2022 · 2022
Later among the works it cites.
Synthetic text generation with differential privacy: A simple and practical recipe
Xiang Yue, Huseyin A Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Hoda Shajari, Huan Sun, David Levitan, and Robert Sim. 2022 · 2022
Later among the works it cites.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Later among the works it cites.
Individualized pate: Differentially private machine learning with individual privacy guarantees
Franziska Boenisch, Christopher Mühl, Roy Rinberg, Jannis Ihrig, and Adam Dziedzic. 2023 · 2023
Later among the works it cites.
On the convergence and calibration of deep learning with differential privacy
Zhiqi Bu, Hua Wang, Zongyu Dai, and Qi Long. 2023 · 2023
Later among the works it cites.
Flocks of stochastic parrots: Differentially private prompt learning for large language models
Haonan Duan, Adam Dziedzic, Nicolas Papernot, and Franziska Boenisch. 2023 · 2023
Later among the works it cites.
How to choose" good" samples for text data augmentation
Xiaotian Lin, Nankai Lin, Yingwen Fu, Ziyu Yang, and Shengyi Jiang. 2023 · 2023
Later among the works it cites.
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, et al. 2023 · 2023
Later among the works it cites.
Privacy-preserving in-context learning for large language models
Tong Wu, Ashwinee Panda, Jiachen T Wang, and Prateek Mittal. 2023 · 2023
Later among the works it cites.