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Dataset distillation aims to learn a small synthetic dataset that preserves most of the information from the original dataset.
Three scenarios for continual learning
Gido M. van de Ven and Andreas S. Tolias · 1904
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Improving dataset distillation
Ilia Sucholutsky and Matthias Schonlau · 1910
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Backpropagation through time: what it does and how to do it
P.J. Werbos · 1990
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Bayesian learning for neural networks, 1995
Radford M. Neal · 1995
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Catastrophic forgetting, rehearsal and pseudorehearsal
Anthony V. Robins · 1995
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Catastrophic forgetting in connectionist networks
Robert French · 1999
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Flexible dataset distillation: Learn labels instead of images
Ondrej Bohdal, Yongxin Yang, and Timothy M. Hospedales · 2006
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Federated learning via synthetic data
Jack Goetz and Ambuj Tewari · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Super-samples from kernel herding
Yutian Chen, Max Welling, and Alexander J. Smola · 2010
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The caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomás Mikolov, and Yoshua Bengio · 2013
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Training recurrent neural networks
Ilya Sutskever · 2013
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Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan P. Adams · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Tiny imagenet visual recognition challenge
Ya Le and Xuan S. Yang · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor S. Lempitsky · 2016
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2016
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, and Christoph H. Lampert · 2016
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Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes · 2019
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Memguard: Defending against black-box membership inference attacks via adversarial examples
Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, and Neil Zhenqiang Gong · 2019
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Optimizing millions of hyperparameters by implicit differentiation
Jonathan Lorraine, Paul Vicol, and David Duvenaud · 2020
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A smaller subset of 10 easily classified classes from imagenet, and a little more french, 2020
Jeremy Howard · 2020
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Gdumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip H. S. Torr, and Puneet K. Dokania · 2020
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Takashi Fukuda, Masayuki Suzuki, Gakuto Kurata, Samuel Thomas, Jia Cui, and Bhuvana Ramabhadran · 2017
Cited alongside, same era.
Unbiasing truncated backpropagation through time
Corentin Tallec and Yann Ollivier · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Cited alongside, same era.
Model compression via distillation and quantization
Antonio Polino, Razvan Pascanu, and Dan Alistarh · 2018
Cited alongside, same era.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A. Efros · 2018
Cited alongside, same era.
Understanding short-horizon bias in stochastic meta-optimization
Yuhuai Wu, Mengye Ren, Renjie Liao, and Roger B. Grosse · 2018
Cited alongside, same era.
Mnemonics training: Multi-class incremental learning without forgetting
Yaoyao Liu, Yuting Su, An-An Liu, Bernt Schiele, and Qianru Sun · 2020
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Flax: A neural network library and ecosystem for JAX, 2020
Jonathan Heek, Anselm Levskaya, Avital Oliver, Marvin Ritter, Bertrand Rondepierre, Andreas Steiner, and Marc van Zee · 2020
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Large batch optimization for deep learning: Training BERT in 76 minutes
Yang You, Jing Li, Sashank J. Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, and Cho-Jui Hsieh · 2020
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
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Distilled replay: Overcoming forgetting through synthetic samples
Andrea Rosasco, Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, and Davide Bacciu · 2021
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Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
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Soft-label anonymous gastric x-ray image distillation
Guang Li, Ren Togo, Takahiro Ogawa, and Miki Haseyama · 2021
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Unbiased gradient estimation in unrolled computation graphs with persistent evolution strategies
Paul Vicol, Luke Metz, and Jascha Sohl-Dickstein · 2021
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Dataset meta-learning from kernel ridge-regression
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee · 2021
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Dataset distillation with infinitely wide convolutional networks
Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee · 2021
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Rethinking experience replay: a bag of tricks for continual learning
Pietro Buzzega, Matteo Boschini, Angelo Porrello, and Simone Calderara · 2021
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Adaptive aggregation networks for class-incremental learning
Yaoyao Liu, Bernt Schiele, and Qianru Sun · 2021
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Dataset condensation with contrastive signals
Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, and Sungroh Yoon · 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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Dataset distillation by matching training trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu · 2022
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High-dimensional asymptotics of feature learning: How one gradient step improves the representation
Jimmy Ba, Murat A Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, and Greg Yang · 2022
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URL https://github.com/tensorflow/privacy
tensorflow/privacy: library for training machine learning models with privacy for training data, 2022 · 2022
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Tensor programs V: tuning large neural networks via zero-shot hyperparameter transfer
Greg Yang, Edward J. Hu, Igor Babuschkin, Szymon Sidor, Xiaodong Liu, David Farhi, Nick Ryder, Jakub Pachocki, Weizhu Chen, and Jianfeng Gao · 2022
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