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A recent trend in explainable AI research has focused on surrogate modeling, where neural networks are approximated as simpler ML algorithms such as kernel machines.
HuggingFace’s Transformers: State-of-the-art Natural Language Processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 1910
Earlier work this paper cites.
Grundzüge einer allgemeinen Theorie der linearen Integralgleichungen
David Hilbert · 1912
Earlier work this paper cites.
Extensions of lipschitz mappings into a hilbert space
William B. Johnson and Joram Lindenstrauss · 1984
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
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Earlier work this paper cites.
The nature of statistical learning theory
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Earlier work this paper cites.
Learning with kernels: support vector machines, regularization, optimization, and beyond
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Earlier work this paper cites.
Kernel methods in machine learning
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Earlier work this paper cites.
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Earlier work this paper cites.
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A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
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Matthew L. Leavitt and Ari Morcos · 2010
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Rectified linear units improve restricted Boltzmann machines
Vinod Nair and Geoffrey E. Hinton · 2010
Earlier work this paper cites.
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Mauricio A. Alvarez, Lorenzo Rosasco, and Neil D. Lawrence · 2011
Earlier work this paper cites.
Introduction to Real Analysis (4th Edition)
Robert G. Bartle and Donald R. Sherbert · 2011
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Decoupled Weight Decay Regularization
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
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Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Earlier work this paper cites.
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Randall Balestriero and Richard Baraniuk · 2018
Earlier work this paper cites.
On lazy training in differentiable programming
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Earlier work this paper cites.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Alex Warstadt, Amanpreet Singh, and Samuel R. Bowman · 2018
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This looks like that: deep learning for interpretable image recognition
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Philip M Long · 2021
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What can linearized neural networks actually say about generalization?
Guillermo Ortiz-Jiménez, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2021
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huyvnphan/pytorch_cifar10, January 2021
Huy Phan · 2021
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Deep Adversarially-Enhanced k-Nearest Neighbors
Ren Wang, Tianqi Chen, and Alfred Hero · 2021
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Greg Yang and Edward J. Hu · 2021
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Towards tracing knowledge in language models back to the training data
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BadNets: Evaluating backdooring attacks on deep neural networks
Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2019
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Regularizing neural networks via minimizing hyperspherical energy
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Ekin Akyürek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, and Kelvin Guu · 2022
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Neural networks as kernel learners: The silent alignment effect
Alexander Atanasov, Blake Bordelon, and Cengiz Pehlevan · 2022
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When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations
Xiangning Chen, Cho-Jui Hsieh, and Boqing Gong · 2022
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p p -DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations
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Evolution of neural tangent kernels under benign and adversarial training, 2022
Noel Loo, Ramin Hasani, Alexander Amini, and Daniela Rus · 2022
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A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel
Mohamad Amin Mohamadi and Danica J. Sutherland · 2022
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Fast finite width neural tangent kernel
Roman Novak, Jascha Sohl-Dickstein, and Samuel S Schoenholz · 2022
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Feature learning in neural networks and kernel machines that recursively learn features
Adityanarayanan Radhakrishnan, Daniel Beaglehole, Parthe Pandit, and Mikhail Belkin · 2022
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Poison forensics: Traceback of data poisoning attacks in neural networks
Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, and Ben Y Zhao · 2022
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Limitations of the NTK for Understanding Generalization in Deep Learning
Nikhil Vyas, Yamini Bansal, and Preetum Nakkiran · 2022
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Spectral evolution and invariance in linear-width neural networks
Zhichao Wang, Andrew Engel, Anand Sarwate, Ioana Dumitriu, and Tony Chiang · 2022
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More than a toy: Random matrix models predict how real-world neural representations generalize
Alexander Wei, Wei Hu, and Jacob Steinhardt · 2022
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What learning algorithm is in-context learning? investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou · 2023
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An exact kernel equivalence for finite classification models, 2023
Brian Bell, Michael Geyer, David Glickenstein, Amanda Fernandez, and Juston Moore · 2023
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TRAK: Attributing Model Behavior at Scale
Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, and Aleksander Madry · 2023
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Efficient kernel surrogates for neural network-based regression
Saad Qadeer, Andrew Engel, Adam Tsou, Max Vargas, Panos Stinis, and Tony Chiang · 2023
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Sample based explanations via generalized representers
Che-Ping Tsai, Chih-Kuan Yeh, and Pradeep Ravikumar · 2023
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What can the neural tangent kernel tell us about adversarial robustness?, 2023
Nikolaos Tsilivis and Julia Kempe · 2023
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