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We introduce an asymmetric distance in the space of learning tasks, and a framework to compute their complexity.
Keeping neural networks simple by minimizing the description length of the weights
Geoffrey Hinton and Drew Van Camp · 1993
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Controllers for reachability specifications for hybrid systems
John Lygeros, Claire Tomlin, and Shankar Sastry · 1999
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The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
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Kolmogorov’s structure functions and model selection
Nikolai K Vereshchagin and Paul MB Vitányi · 2004
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Data complexity in machine learning and novel classification algorithms
Ling Li · 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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A PAC-Bayesian tutorial with a dropout bound
David McAllester · 2013
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New insights and perspectives on the natural gradient method
James Martens · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Domain adaptation for visual applications: A comprehensive survey
Gabriela Csurka · 2017
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Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data
Gintare Karolina Dziugaite and Daniel M Roy · 2017
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The dynamics of differential learning I: Information-dynamics and task reachability
Alessandro Achille, Glen Mbeng, and Stefano Soatto · 2018
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Emergence of invariance and disentanglement in deep representations
Alessandro Achille and Stefano Soatto · 2018
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Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
Pratik Chaudhari and Stefano Soatto · 2018
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Taskonomy: Disentangling task transfer learning
Amir R Zamir, Alexander Sax, William Shen, Leonidas J Guibas, Jitendra Malik, and Silvio Savarese · 2018
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Task2vec: Task embedding for meta-learning
Alessandro Achille, Michael Lam, Rahul Tewari, Avinash Ravichandran, Subhransu Maji, Charless C Fowlkes, Stefano Soatto, and Pietro Perona · 2019
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Cited alongside, same era.
Critical learning periods in deep neural networks
Alessandro Achille, Matteo Rovere, and Stefano Soatto · 2019
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