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Transferability of learned features between tasks can massively reduce the cost of training a neural network on a novel task.
Visual receptive fields of neurons in inferotemporal cortex of the monkey
Charles G Gross, David B Bender, and Carlos Eduardo de Rocha-Miranda · 1969
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Visual receptive fields of frontal eye field neurons
Charles W Mohler, Michael E Goldberg, and Robert H Wurtz · 1973
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Priors for Infinite Networks
Radford M. Neal · 1996
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Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds
Frédéric E Theunissen, Kamal Sen, and Allison J Doupe · 2000
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Visualizing higher-layer features of a deep network
Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2009
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I Jordan · 2015
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Inceptionism: Going deeper into neural networks
Alexander Mordvintsev, Christopher Olah, and Mike Tyka · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
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Toward deeper understanding of neural networks: The power of initialization and a dual view on expressivity
Amit Daniely, Roy Frostig, and Yoram Singer · 2016
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune · 2016
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Sgd learns the conjugate kernel class of the network
Amit Daniely · 2017
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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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Exploring generalization in deep learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro · 2017
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Plug & play generative networks: Conditional iterative generation of images in latent space
Gradient Descent Finds Global Minima of Deep Neural Networks
Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai · 2018
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Gradient Descent Provably Optimizes Over-parameterized Neural Networks
Simon S. Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Deep neural networks as gaussian processes
Jaehoon Lee, Jascha Sohl-dickstein, Jeffrey Pennington, Roman Novak, Sam Schoenholz, and Yasaman Bahri · 2018
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Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data
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Anh Nguyen, Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, and Jason Yosinski · 2017
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Opening the black box of deep neural networks via information. 2017
R Schwartz-Ziv and N Tishby · 2017
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Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
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A Convergence Theory for Deep Learning via Over-Parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2018
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On Lazy Training in Differentiable Programming
Lenaic Chizat, Edouard Oyallon, and Francis Bach · 2018
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Gaussian process behaviour in wide deep neural networks
Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, and Zoubin Ghahramani · 2018
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Yuanzhi Li and Yingyu Liang · 2018
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Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
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Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks
Difan Zou, Yuan Cao, Dongruo Zhou, and Quanquan Gu · 2018
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Activation atlas
Shan Carter, Zan Armstrong, Ludwig Schubert, Ian Johnson, and Chris Olah · 2019
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Scaling description of generalization with number of parameters in deep learning
Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d’Ascoli, Giulio Biroli, Clément Hongler, and Matthieu Wyart · 2019
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Rapid learning or feature reuse? towards understanding the effectiveness of maml
Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals · 2019
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