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We study the problem of dataset distillation - creating a small set of synthetic examples capable of training a good model.
Gradient-based learning applied to document recognition
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Core vector machines: fast SVM training on very large data sets
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Object detection with discriminatively trained part-based models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., and Ramanan, D. (2010) · 2010
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A review of instance selection methods
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The Caltech-UCSD Birds-200-2011 dataset
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ImageNet classification with deep convolutional neural networks
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The matrix cookbook
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Are all training examples equally valuable?
Lapedriza, A., Pirsiavash, H., Bylinskii, Z., and Torralba, A. (2013) · 2013
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J. (2014) · 2014
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Gradient-based Hyperparameter Optimization through Reversible Learning
Maclaurin, D., Duvenaud, D., and Adams, R. P. (2015) · 2015
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Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
Luketina, J., Berglund, M., Klaus Greff, A., and Raiko, T. (2016) · 2016
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Rethinking the Inception architecture for computer vision
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Practical coreset constructions for machine learning
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Model-agnostic meta-learning for fast adaptation of deep networks
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Regularizing neural networks by penalizing confident output distributions
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MetaReg: towards domain generalization using meta-regularization
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Neural architecture search: a survey
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Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Estimators for Reinforcement Learning
Farquhar, G., Whiteson, S., and Foerster, J. (2019) · 2019
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Meta-learning with differentiable convex optimization
Lee, K., Maji, S., Ravichandran, A., and Soatto, S. (2019) · 2019
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Feature-critic networks for heterogeneous domain generalization
Li, Y., Yang, Y., Zhou, W., and Hospedales, T. M. (2019) · 2019
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Taming MAML: Efficient Unbiased Meta-Reinforcement Learning
Liu, H., Socher, R., and Xiong, C. (2019) · 2019
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Zero-shot knowledge transfer via adversarial belief matching
Micaelli, P. and Storkey, A. (2019) · 2019
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Deep learning for classical Japanese literature
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On first-order meta-learning algorithms
Nichol, A., Achiam, J., and Schulman, J. (2018) · 2018
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Active learning for convolutional neural networks: A core-set approach
Sener, O. and Savarese, S. (2018) · 2018
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Dataset distillation
Wang, T., Zhu, J.-Y., Torralba, A., and Efros, A. A. (2018) · 2018
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Privacy-preserving machine learning: threats and solutions
Al-Rubaie, M. and Chang, J. M. (2019) · 2019
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Meta-learning with differentiable closed-form solvers
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Green AI
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Proxy Datasets for Training Convolutional Neural Networks
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Soft-label dataset distillation and text dataset distillation
Sucholutsky, I. and Schonlau, M. (2019) · 2019
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Meta-learning in neural networks: a survey
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Optimizing Millions of Hyperparameters by Implicit Differentiation
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’Less Than One’-Shot Learning: Learning N Classes From M<N Samples
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