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As the state-of-the-art machine learning methods in many fields rely on larger datasets, storing datasets and training models on them become significantly more expensive.
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Approximating extent measures of points
Pankaj K Agarwal, Sariel Har-Peled, and Kasturi R Varadarajan · 2004
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On coresets for k-means and k-median clustering
Sariel Har-Peled and Soham Mazumdar · 2004
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Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Learning optimized map estimates in continuously-valued mrf models
Kegan GG Samuel and Marshall F Tappen · 2009
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Herding dynamical weights to learn
Max Welling · 2009
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Facility location: concepts, models, algorithms and case studies
G W Wolf · 2011
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Generic methods for optimization-based modeling
Justin Domke · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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A generative process for sampling contractive auto-encoders
Salah Rifai, Yoshua Bengio, Yann Dauphin, and Pascal Vincent · 2012
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Turning big data into tiny data: Constant-size coresets for k-means, pca and projective clustering
Dan Feldman, Melanie Schmidt, and Christian Sohler · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Rectifier nonlinearities improve neural network acoustic models
Andrew L Maas, Awni Y Hannun, and Andrew Y Ng · 2013
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On rectified linear units for speech processing
Matthew D. Zeiler, Marc’Aurelio Ranzato, Rajat Monga, Mark Z. Mao, Kyeongcheol Yang, Quoc V. Le, Patrick Nguyen, Andrew W. Senior, Vincent Vanhoucke, Jeffrey Dean, and Geoffrey E. Hinton · 2013
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Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Group normalization
Yuxin Wu and Kaiming He · 2018
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Gradient based sample selection for online continual learning
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio · 2019
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Zero-shot knowledge distillation in deep networks
Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, and Anirban Chakraborty · 2019
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Distilling the knowledge in a neural network
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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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Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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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 Lempitsky · 2016
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Gradient matching generative networks for zero-shot learning
Mert Bulent Sariyildiz and Ramazan Gokberk Cinbis · 2019
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Soft-label dataset distillation and text dataset distillation
Ilia Sucholutsky and Matthias Schonlau · 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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Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
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Ligeng Zhu, Zhijian Liu, and Song Han · 2019
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Scail: Classifier weights scaling for class incremental learning
Eden Belouadah and Adrian Popescu · 2020
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Flexible dataset distillation: Learn labels instead of images
Ondrej Bohdal, Yongxin Yang, and Timothy Hospedales · 2020
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Federated learning via synthetic data
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Soft-label anonymous gastric x-ray image distillation
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Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 2020
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Distilled one-shot federated learning
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