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Dataset distillation extracts a small set of synthetic training samples from a large dataset with the goal of achieving competitive performance on test data when trained on this sample.
An efficient gradient-based algorithm for on-line training of recurrent network trajectories
Ronald J Williams and Jing Peng · 1990
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Truncated backpropagation through time and kalman filter training for neurocontrol
GV Puskorius and LA Feldkamp · 1994
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Clément Hongler, and Franck Gabriel · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A. Efros · 2018
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On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, and Ruosong Wang · 2019
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Limitations of lazy training of two-layers neural network
Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, and Andrea Montanari · 2019
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Generalized inner loop meta-learning
Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, and Soumith Chintala · 2019
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Introduction to coresets: Accurate coresets
Ibrahim Jubran, Alaa Maalouf, and Dan Feldman · 2019
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Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
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Truncated back-propagation for bilevel optimization
Amirreza Shaban, Ching-An Cheng, Nathan Hatch, and Byron Boots · 2019
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An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J. Gordon · 2019
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Bilevel optimization: theory, algorithms, applications and a bibliography
Stephan Dempe · 2020
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Transferred discrepancy: Quantifying the difference between representations
Yunzhen Feng, Runtian Zhai, Di He, Liwei Wang, and Bin Dong · 2020
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Optimizing millions of hyperparameters by implicit differentiation
Jonathan Lorraine, Paul Vicol, and David Duvenaud · 2020
Cited alongside, same era.
Coresets for data-efficient training of machine learning models
Baharan Mirzasoleiman, Jeff Bilmes, and Jure Leskovec · 2020
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Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data
Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, and Jeffrey Clune · 2020
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Deep learning on a data diet: Finding important examples early in training
Mansheej Paul, Surya Ganguli, and Gintare Karolina Dziugaite · 2021
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Soft-label dataset distillation and text dataset distillation
Ilia Sucholutsky and Matthias Schonlau · 2021
Cited alongside, same era.
Neural tangent generalization attacks
What can the neural tangent kernel tell us about adversarial robustness?
Nikolaos Tsilivis and Julia Kempe · 2022
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Can we achieve robustness from data alone?
Nikolaos Tsilivis, Jingtong Su, and Julia Kempe · 2022
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CAFE: Learning to condense dataset by aligning features
Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Shuo Yang, Shuo Wang, Guan Huang, Hakan Bilen, Xinchao Wang, and Yang You · 2022
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Accelerating dataset distillation via model augmentation
Lei Zhang, Jie Zhang, Bowen Lei, Subhabrata Mukherjee, Xiang Pan, Bo Zhao, Caiwen Ding, Yao Li, and Dongkuan Xu · 2022
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Dataset distillation using neural feature regression
Yongchao Zhou, Ehsan Nezhadarya, and Jimmy Ba · 2022
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Generalizing dataset distillation via deep generative prior
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Chia-Hung Yuan and Shan-Hung Wu · 2021
Cited alongside, same era.
Dataset distillation by matching training trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu · 2022
Cited alongside, same era.
Dc-bench: Dataset condensation benchmark
Justin Cui, Ruochen Wang, Si Si, and Cho-Jui Hsieh · 2022
Cited alongside, same era.
Remember the past: Distilling datasets into addressable memories for neural networks
Zhiwei Deng and Olga Russakovsky · 2022
Cited alongside, same era.
Fedsynth: Gradient compression via synthetic data in federated learning
Shengyuan Hu, Jack Goetz, Kshitiz Malik, Hongyuan Zhan, Zhe Liu, and Yue Liu · 2022
Cited alongside, same era.
Delving into effective gradient matching for dataset condensation
Zixuan Jiang, Jiaqi Gu, Mingjie Liu, and David Z. Pan · 2022
Cited alongside, same era.
Dataset condensation via efficient synthetic-data parameterization
Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, and Hyun Oh Song · 2022
Cited alongside, same era.
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu · 2023
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Scaling up dataset distillation to imagenet-1k with constant memory
Justin Cui, Ruochen Wang, Si Si, and Cho-Jui Hsieh · 2023
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Minimizing the accumulated trajectory error to improve dataset distillation
Jiawei Du, Yidi Jiang, Vincent T. F. Tan, Joey Tianyi Zhou, and Haizhou Li · 2023
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A survey on dataset distillation: Approaches, applications and future directions
Zongxion Geng, Jiahui andg Chen, Yuandou Wang, Herbert Woisetschlaeger, Sonja Schimmler, Ruben Mayer, Zhiming Zhao, and Chunming Rong · 2023
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A comprehensive survey to dataset distillation
Shiye Lei and Dacheng Tao · 2023
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Dataset distillation with convexified implicit gradients
Noel Loo, Ramin Hasani, Mathias Lechner, and Daniela Rus · 2023
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Noveen Sachdeva and Julian McAuley · 2023
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Loss-curvature matching for dataset selection and condensation
Seungjae Shin, Heesun Bae, Donghyeok Shin, Weonyoung Joo, and Il-Chul Moon · 2023
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Dim: Distilling dataset into generative model
Kai Wang, Jianyang Gu, Daquan Zhou, Zheng Zhu, Wei Jiang, and Yang You · 2023
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A comprehensive survey to dataset distillation
Ruonan Yu, Songhua Liu, and Xinchao Wang · 2023
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Dataset condensation with distribution matching
Bo Zhao and Hakan Bilen · 2023
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