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Conventional wisdom suggests that neural network predictions tend to be unpredictable and overconfident when faced with out-of-distribution (OOD) inputs.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
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Covariate shift adaptation by importance weighted cross validation
Masashi Sugiyama, Matthias Krauledat, and Klaus-Robert Müller · 2007
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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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Covariate shift by kernel mean matching
Arthur Gretton, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt, and Bernhard Schölkopf · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Towards better selective classification
Leo Feng, Mohamed Osama Ahmed, Hossein Hajimirsadeghi, and Amir H Abdi · 2011
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Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
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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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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Path-sgd: Path-normalized optimization in deep neural networks
Behnam Neyshabur, Russ R Salakhutdinov, and Nati Srebro · 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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Learning with rejection
Corinna Cortes, Giulia DeSalvo, and Mehryar Mohri · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 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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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
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Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky · 2017
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Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Age progression/regression by conditional adversarial autoencoder
Zhifei Zhang, Yang Song, and Hairong Qi · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced
Simon S Du, Wei Hu, and Jason D Lee · 2018
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Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2018
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Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2018
Learning representations that support extrapolation
Taylor Webb, Zachary Dulberg, Steven Frankland, Alexander Petrov, Randall O’Reilly, and Jonathan Cohen · 2020
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How neural networks extrapolate: From feedforward to graph neural networks
Keyulu Xu, Mozhi Zhang, Jingling Li, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2020
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Learning in high dimension always amounts to extrapolation
Randall Balestriero, Jerome Pesenti, and Yann LeCun · 2021
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Classification with rejection based on cost-sensitive classification
Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, and Masashi Sugiyama · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer · 2021
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The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, and Nathan Srebro · 2018
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Implicit regularization in deep matrix factorization
Sanjeev Arora, Nadav Cohen, Wei Hu, and Yuping Luo · 2019
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Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem
Matthias Hein, Maksym Andriushchenko, and Julian Bitterwolf · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Gradient descent maximizes the margin of homogeneous neural networks
Kaifeng Lyu and Jian Li · 2019
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On the calibration of multiclass classification with rejection
Chenri Ni, Nontawat Charoenphakdee, Junya Honda, and Masashi Sugiyama · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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The low-rank simplicity bias in deep networks
Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, and Phillip Isola · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
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Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
John P Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, and Ludwig Schmidt · 2021
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Understanding softmax confidence and uncertainty
Tim Pearce, Alexandra Brintrup, and Jun Zhu · 2021
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From local structures to size generalization in graph neural networks
Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, and Haggai Maron · 2021
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Agreement-on-the-line: Predicting the performance of neural networks under distribution shift
Christina Baek, Yiding Jiang, Aditi Raghunathan, and J Zico Kolter · 2022
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On the implicit bias of gradient descent for temporal extrapolation
Edo Cohen-Karlik, Avichai Ben David, Nadav Cohen, and Amir Globerson · 2022
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The familiarity hypothesis: Explaining the behavior of deep open set methods
Thomas G Dietterich and Alex Guyer · 2022
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Sgd and weight decay provably induce a low-rank bias in neural networks
Tomer Galanti, Zachary S Siegel, Aparna Gupte, and Tomaso Poggio · 2022
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Don’t forget the nullspace! nullspace occupancy as a mechanism for out of distribution failure
Daksh Idnani, Vivek Madan, Naman Goyal, David J Schwab, and Shanmukha Ramakrishna Vedantam · 2022
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Out-of-distribution detection with deep nearest neighbors
Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li · 2022
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Extrapolation and spectral bias of neural nets with hadamard product: a polynomial net study
Yongtao Wu, Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, and Volkan Cevher · 2022
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Augmenting softmax information for selective classification with out-of-distribution data
Guoxuan Xia and Christos-Savvas Bouganis · 2022
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How reliable is your regression model’s uncertainty under real-world distribution shifts?
Fredrik K Gustafsson, Martin Danelljan, and Thomas B Schön · 2023
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Implicit regularization towards rank minimization in relu networks
Nadav Timor, Gal Vardi, and Ohad Shamir · 2023
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