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In this paper, we propose a novel data-pruning approach called moving-one-sample-out (MoSo), which aims to identify and remove the least informative samples from the training set.
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Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia: Selection via proxy: Efficient data selection for deep learning. In: International Conference on Learning Representations (2019)
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Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal: Deep batch active learning by diverse, uncertain gradient lower bounds. In: International Conference on Learning Representations (2019)
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Mingxing Tan, Quoc Le: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning (2019)
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Baharan Mirzasoleiman, Jeff Bilmes, Jure Leskovec: Coresets for data-efficient training of machine learning models. In: International Conference on Machine Learning (2020)
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Baharan Mirzasoleiman, Kaidi Cao, Jure Leskovec: Coresets for robust training of deep neural networks against noisy labels. In: Advances in Neural Information Processing Systems (2020)
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Christoph Schuhmann, Romain Beaumont, Richard Vencu, et. al: LAION-5b: An open large-scale dataset for training next generation image-text models. In: Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2022)
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George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, Jun-Yan Zhu: Dataset distillation by matching training trajectories. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)
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Jaehong Yoon, Divyam Madaan, Eunho Yang, Sung Ju Hwang: Online coreset selection for rehearsal-based continual learning. In: International Conference on Learning Representations (2022)
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Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, Hyun Oh Song: Dataset condensation via efficient synthetic-data parameterization. In: International Conference on Machine Learning (2022)
2022
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Daphna Weinshall, Dan Amir: Theory of curriculum learning, with convex loss functions. Journal of Machine Learning Research 21
2020
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Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, et. al: Language models are few-shot learners. In: Advances in Neural Information Processing Systems (2020)
2020
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Vitaly Feldman, Chiyuan Zhang: What neural networks memorize and why: Discovering the long tail via influence estimation. In: Advances in Neural Information Processing Systems (2020)
2020
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2021
Cited alongside, same era.
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Abir De, Rishabh Iyer: Grad-match: Gradient matching based data subset selection for efficient deep model training. In: International Conference on Machine Learning (2021)
2021
Cited alongside, same era.
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Rishabh Iyer: Generalization based data subset selection for efficient and robust learning. In: Proceedings of the AAAI Conference on Artificial Intelligence (2021)
2021
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Krishnateja Killamsetty, Xujiang Zhao, Feng Chen, Rishabh Iyer: Retrieve: Coreset selection for efficient and robust semi-supervised learning. In: Advances in Neural Information Processing Systems (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Later among the works it cites.
Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh: Dc-bench: Dataset condensation benchmark. In: Advances in Neural Information Processing Systems (2022)
2022
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Pan Lu, Swaroop Mishra, Tony Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Pete Clark, Ashwin Kalyan: Learn to explain: Multimodal reasoning via thought chains for science question answering. In: Advances in Neural Information Processing Systems (2022)
2022
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2022
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Sorscher, B., Geirhos, R., Shekhar, S., Ganguli, S., Morcos, A.: Beyond neural scaling laws: beating power law scaling via data pruning. In: Advances in Neural Information Processing Systems (2022)
2022
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Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan, Jeff Bilmes, Rishabh Iyer: Prism: A unified framework of parameterized submodular information measures for targeted data subset selection and summarizationg. In: Proceedings of the AAAI Conference on Artificial Intelligence (2022)
2022
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Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, Neil Shah: Graph condensation for graph neural networks. In: International Conference on Learning Representations (2022)
2022
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Zhiwei Deng, Olga Russakovsky: Remember the past: Distilling datasets into addressable memories for neural networks. In: Advances in Neural Information Processing Systems (2022)
2022
Later among the works it cites.
2023
Closest in time.
Haizhong Zheng, Rui Liu, Fan Lai, Atul Prakash: Coverage-centric coreset selection for high pruning rates. In: International Conference on Learning Representations (2023)
2023
Closest in time.
2023
Closest in time.
Shuo Yang, Zeke Xie, Hanyu Peng, Min Xu, Mingming Sun, Ping Li: Dataset pruning: Reducing training data by examining generalization influence. In: International Conference on Learning Representations (2023)
2023
Closest in time.
Xiaobo Xia, Jiale Liu, Jun Yu, Xu Shen, Bo Han, Tongliang Liu: Moderate coreset: A universal method of data selection for real-world data-efficient deep learning. In: International Conference on Learning Representations (2023)
2023
Closest in time.