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We address efficient calculation of influence functions for tracking predictions back to the training data.
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Lapuschkin, S.; Wäldchen, S.; Binder, A.; Montavon, G.; Samek, W.; and Müller, K. 2019 · 1902
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The principle of minimized iterations in the solution of the matrix eigenvalue problem
Arnoldi, W. E. 1951 · 1951
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Characterizations of an Empirical Influence Function for Detecting Influential Cases in Regression
Cook, R. D.; and Weisberg, S. 1980 · 1980
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Residuals and influence in regression
Cook, R. D.; and Weisberg, S. 1982 · 1982
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MNIST handwritten digit database
LeCun, Y.; Cortes, C.; and Burges, C. 1994 · 1994
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Fast Exact Multiplication by the Hessian
Pearlmutter, B. A. 1994 · 1994
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Numerical Linear Algebra
Trefethen, L. N.; and Bau, D. 1997 · 1997
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Large scale parallel document mining for machine translation
Uszkoreit, J.; Ponte, J.; Popat, A.; and Dubiner, M. 2010 · 2010
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K.; Vedaldi, A.; and Zisserman, A. 2013 · 2013
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Sketching as a Tool for Numerical Linear Algebra
Woodruff, D. P. 2014 · 2014
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Understanding Neural Networks through Representation Erasure
Li, J.; Monroe, W.; and Jurafsky, D. 2016 · 2016
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”Why should I trust you?” Explaining the predictions of any classifier
Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2016 · 2016
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“Influence sketching”: Finding influential samples in large-scale regressions
Wojnowicz, M.; Cruz, B.; Zhao, X.; Wallace, B.; Wolff, M.; Luan, J.; and Crable, C. 2016 · 2016
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Second-Order Stochastic Optimization for Machine Learning in Linear Time
Agarwal, N.; Bullins, B.; and Hazan, E. 2017 · 2017
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Understanding Black-box Predictions via Influence Functions
Koh, P. W.; and Liang, P. 2017 · 2017
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Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Automatic differentiation in machine learning: a survey
Baydin, A. G.; Pearlmutter, B. A.; Radul, A. A.; and Siskind, J. M. 2018 · 2018
Cited alongside, same era.
Alibaba’s Neural Machine Translation Systems for WMT18
Deng, Y.; Cheng, S.; Lu, J.; Song, K.; Wang, J.; Wu, S.; Yao, L.; Zhang, G.; et al. 2018 · 2018
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The AFRL WMT18 Systems: Ensembling, Continuation and Combination
Gwinnup, J.; Anderson, T.; Erdmann, G.; and Young, K. 2018 · 2018
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Microsoft’s Submission to the WMT2018 News Translation Task: How I Learned to Stop Worrying and Love the Data
Junczys-Dowmunt, M. 2018 · 2018
RelatIF: Identifying Explanatory Training Samples via Relative Influence
Barshan, E.; Brunet, M.; and Dziugaite, G. K. 2020 · 2020
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Multi-Stage Influence Function
Chen, H.; Si, S.; Li, Y.; Chelba, C.; Kumar, S.; Boning, D.; and Hsieh, C.-J. 2020 · 2020
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Does Learning Require Memorization? A Short Tale about a Long Tail
Feldman, V. 2020 · 2020
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What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
Feldman, V.; and Zhang, C. 2020 · 2020
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Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions
Han, X.; Wallace, B. C.; and Tsvetkov, Y. 2020 · 2020
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Influence Functions Do Not Seem to Predict Usefulness in NLP Transfer Learning
Kocijan, V.; and Bowman, S. 2020 · 2020
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SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing
Kudo, T.; and Richardson, J. 2018 · 2018
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Mesh-tensorflow: Deep learning for supercomputers
Shazeer, N.; Cheng, Y.; Parmar, N.; Tran, D.; Vaswani, A.; Koanantakool, P.; Hawkins, P.; Lee, H.; et al. 2018 · 2018
Cited alongside, same era.
Data Dropout: Optimizing Training Data for Convolutional Neural Networks
Wang, T.; Huan, J.; and Li, B. 2018 · 2018
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Representer Point Selection for Explaining Deep Neural Networks
Yeh, C.; Kim, J. S.; Yen, I. E.; and Ravikumar, P. 2018 · 2018
Cited alongside, same era.
A Fast, Compact, Accurate Model for Language Identification of Codemixed Text
Zhang, Y.; Riesa, J.; Gillick, D.; Bakalov, A.; Baldridge, J.; and Weiss, D. 2018 · 2018
Cited alongside, same era.
Understanding the origins of bias in word embeddings
Brunet, M.-E.; Alkalay-Houlihan, C.; Anderson, A.; and Zemel, R. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
Estimating Training Data Influence by Tracing Gradient Descent
Pruthi, G.; Liu, F.; Sundararajan, M.; and Kale, S. 2020 · 2020
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Influence Functions in Deep Learning Are Fragile
Basu, S.; Pope, P.; and Feizi, S. 2021 · 2021
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What Will it Take to Fix Benchmarking in Natural Language Understanding?
Bowman, S. R.; and Dahl, G. 2021 · 2021
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When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations
Chen, X.; Hsieh, C.-J.; and Gong, B. 2021 · 2021
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Sharpness-aware Minimization for Efficiently Improving Generalization
Foret, P.; Kleiner, A.; Mobahi, H.; and Neyshabur, B. 2021 · 2021
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FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging
Guo, H.; Fatema R., N.; Hase, P.; Bansal, M.; and Xiong, C. 2021 · 2021
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Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits
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