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Influence functions approximate the effect of training samples in test-time predictions and have a wide variety of applications in machine learning interpretability and uncertainty estimation.
The proof and measurement of association between two things
C. Spearman · 1904
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On the accuracy of influence functions for measuring group effects
Pang Wei Koh, Kai-Siang Ang, Hubert H. K. Teo, and Percy Liang · 1905
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Iris flower dataset
Anderson · 1936
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Characterizations of an empirical influence function for detecting influential cases in regression
R. Dennis Cook and Sanford Weisberg · 1980
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Nonlinear experiments: Optimal design and inference based on likelihood
Probal Chaudhuri and Per A. Mykland · 1993
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Fast exact multiplication by the hessian
Barak A. Pearlmutter · 1994
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An introduction to the conjugate gradient method without the agonizing pain
Jonathan R Shewchuk · 1994
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Gradient-based learning applied to document recognition
Yann Lecun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Cifar-10 (canadian institute for advanced research)
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2000
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Machine learning in automated text categorization
Fabrizio Sebastiani · 2002
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Pearson’s Correlation Coefficient , pp. 1090–1091
Wilhelm Kirch (ed.) · 2008
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Large-scale machine learning with stochastic gradient descent
Léon Bottou · 2010
Cited alongside, same era.
Computer Vision: Algorithms and Applications
Richard Szeliski · 2010
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Adam: A method for stochastic optimization, 2014
Diederik P. Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Cited alongside, same era.
Understanding the origins of bias in word embeddings
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, and Richard S. Zemel · 2018
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A swiss army infinitesimal jackknife
Ryan Giordano, Will Stephenson, Runjing Liu, Michael I. Jordan, and Tamara Broderick · 2018
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An overview of deep learning in medical imaging focusing on MRI
Alexander Selvikvåg Lundervold and Arvid Lundervold · 2018
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Bounding and counting linear regions of deep neural networks, 2018
Thiago Serra, Christian Tjandraatmadja, and Srikumar Ramalingam · 2018
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Leslie N. Smith · 2018
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Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin A. Riedmiller · 2015
Cited alongside, same era.
Second order stochastic optimization in linear time
Naman Agarwal, Brian Bullins, and Elad Hazan · 2016
Cited alongside, same era.
An overview of gradient descent optimization algorithms
Sebastian Ruder · 2016
Cited alongside, same era.
Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Y. Zou · 2017
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Accurate, large minibatch SGD: training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross B. Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
Cited alongside, same era.
URL https://dawn.cs.stanford.edu/benchmark/papers/nips17-dawnbench.pdf
DAWNBench: An End-to-End Deep Learning Benchmark and Competition , 2017. Stanford · 2017
Cited alongside, same era.
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Representer point selection for explaining deep neural networks
Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, and Pradeep Ravikumar · 2018
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Second-order group influence functions for black-box predictions
Samyadeep Basu, Xuchen You, and Soheil Feizi · 2019
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A higher-order swiss army infinitesimal jackknife
Ryan Giordano, Michael I. Jordan, and Tamara Broderick · 2019
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Can you trust this prediction? auditing pointwise reliability after learning
Peter G. Schulam and Suchi Saria · 2019
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On second-order group influence functions for black-box predictions
Samyadeep Basu, Xuchen You, and Soheil Feizi · 2020
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Towards faithfully interpretable nlp systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg · 2020
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Estimating training data influence by tracking gradient descent
Garima Pruthi, Frederick Liu, Mukund Sundararajan, and Satyen Kale · 2020
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