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Explainable Artificial Intelligence (XAI) addresses the growing need for transparency and interpretability in AI systems, enabling trust and accountability in decision-making processes.
The use of multiple measurements in taxonomic problems
Ronald A. Fisher · 1936
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Induction of decision trees
J. Ross Quinlan · 1986
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Decision trees and multivariate analysis
J Ross Quinlan · 1986
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Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
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Learning decision trees
J. Ross Quinlan · 1996
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Random forests
Leo Breiman · 2001
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Collective classification in network data
Prithviraj Sen, Galen Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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Consumer credit-risk models via machine-learning algorithms
Amir E. Khandani, Adlar J. Kim, and Andrew W. Lo · 2010
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Artificial Intelligence: A Modern Approach
Stuart Russell and Peter Norvig · 2010
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Bayesian portfolio analysis
Doron Avramov and Guofu Zhou · 2010
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A user’s guide to support vector machines
Asa Ben-Hur and Jason Weston · 2010
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Permutation importance: a corrected feature importance measure
Andre Altmann, Laura Toloşi, Oliver Sander, and Thomas Lengauer · 2010
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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General approach to causal mediation analysis
Kosuke Imai, Luke Keele, and Dustin Tingley · 2010
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Identification, inference, and sensitivity analysis for causal mediation effects
Kosuke Imai, Luke Keele, and Teppei Yamamoto · 2010
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Rule-based systems
Lukasz Kurgan and Petr Musilek · 2011
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The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature
Eric WT Ngai, Yunan Hu, Yijun Wong, Yimin Chen, and Xin Sun · 2011
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Handbook of Markov Chain Monte Carlo
Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng · 2011
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Regression shrinkage and selection via the lasso: a retrospective
Robert Tibshirani · 2011
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Learning word vectors for sentiment analysis
Andrew L Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts · 2011
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Multimodal deep learning
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y Ng · 2011
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An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models
Johan Huysmans, Karel Dejaeger, Christophe Mues, Jan Vanthienen, and Bart Baesens · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Legal reasoning and legal argumentation
Trevor Bench-Capon and Henry Prakken · 2012
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Intelligible models for classification and regression
Yin Lou, Rich Caruana, and Johannes Gehrke · 2012
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Machine learning: a probabilistic perspective
Kevin P Murphy · 2012
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Ensemble methods: foundations and algorithms
Zhi-Hua Zhou · 2012
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
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Applied Logistic Regression
David W Hosmer et al · 2013
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Pattern recognition and machine learning
Christopher M Bishop · 2013
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An Introduction to Statistical Learning
Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani · 2013
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Bayesian data analysis
Andrew Gelman, John B Carlin, Hal S Stern, David B Dunson, Aki Vehtari, and Donald B Rubin · 2013
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Pattern Recognition and Machine Learning
Christopher M Bishop · 2013
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Streaming variational bayes
Tamara Broderick, Nicholas Boyd, Andre Wibisono, Emmanuel Candes, and Michael I Jordan · 2013
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Applied logistic regression
David W Hosmer Jr, Stanley Lemeshow, and Rodney X Sturdivant · 2013
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Understanding variable importances in forests of randomized trees
Gilles Louppe, Louis Wehenkel, Antonio Sutera, and Pierre Geurts · 2013
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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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Learning fair representations
Richard Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Evasion attacks against machine learning at test time
Battista Biggio et al · 2013
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Understanding Machine Learning: From Theory to Algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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Biomedical Informatics: Computer Applications in Health Care and Biomedicine
Edward H Shortliffe and James J Cimino · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Applied linear regression
Sanford Weisberg · 2014
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Data mining with decision trees: theory and applications
Lior Rokach and Oded Maimon · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Machine learning: Trends, perspectives, and prospects
Michael I. Jordan and Tom M. Mitchell · 2015
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad · 2015
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Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model
Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, and David Madigan · 2015
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Expert systems in medical applications: a review
M Durairaj and V Ranjani · 2015
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noga Elhadad · 2015
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Understanding neural networks through deep visualization
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Critical review of recurrent neural networks for sequence learning
Zachary C Lipton, John Berkowitz, and Charles Elkan · 2015
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Visualizing and understanding recurrent networks
Andrej Karpathy, Justin Johnson, and Li Fei-Fei · 2015
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation
Alex Goldstein, Adam Kapelner, Justin Bleich, and Emil Pitkin · 2015
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Explanation in causal inference: Methods for mediation and interaction
Tyler J VanderWeele · 2015
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Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh · 2015
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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Lei Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Richard Zemel, and Yoshua Bengio · 2015
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research
Stefan Lessmann, Bart Baesens, Hian Chye Seow, and Lyn C. Thomas · 2015
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The mythos of model interpretability
Zachary C. Lipton · 2016
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General data protection regulation (gdpr)
European Union · 2016
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Big data’s disparate impact
Solon Barocas and Andrew D. Selbst · 2016
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To predict and serve?
Kristian Lum and William Isaac · 2016
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“why should i trust you?”: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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The mythos of model interpretability
Zachary C. Lipton · 2016
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Rule-based machine learning and knowledge extraction for model interpretability
Rok Piltaver, Mitja Luštrek, Matjaž Gams, and Denis Đonlagić · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 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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50 Years of Game Theory
Lloyd S Shapley · 2016
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Causal inference in statistics: A primer
Judea Pearl, Madelyn Glymour, and Nicholas P Jewell · 2016
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Causal inference in statistics, social, and biomedical sciences
Peter Spirtes, Clark Glymour, and Richard Scheines · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, and Ananthram Swami · 2016
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Causal inference and the data-fusion problem
Elias Bareinboim and Judea Pearl · 2016
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Predicting judicial decisions of the european court of human rights: A natural language processing perspective
Nikolaos Aletras, Dimitrios Tsarapatsanis, Daniel Preoţiuc-Pietro, and Vasileios Lampos · 2016
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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European union regulations on algorithmic decision-making and a “right to explanation”
Bryce Goodman and Seth Flaxman · 2017
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Wojciech Samek, Thomas Wiegand, and Klaus-Robert Müller · 2017
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Unified framework for interpretable methods
Scott M Lundberg and Su-In Lee · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2017
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Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau, and Sebastian Thrun · 2017
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Why a right to explanation of automated decision-making does not exist in the general data protection regulation
Sandra Wachter, Brent Mittelstadt, and Luciano Floridi · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Wojciech Samek, Thomas Wiegand, and Klaus-Robert Müller · 2017
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Generalized Additive Models
Trevor J Hastie and Robert J Tibshirani · 2017
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Generalized Additive Models: An Introduction with R
Simon N Wood · 2017
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Joshua V Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Sundaram Vasudevan, David Moore, Andrew Patton, Alexander Alemi, Matthew D Hoffman, and Rif A Saurous · 2017
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Online controlled experiments and a/b testing
Ron Kohavi and Stefan Thomke · 2017
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Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Research on rule-based expert system and application in fault diagnosis
Jing Wang and Wei Wang · 2017
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Introduction to machine learning: k-nearest neighbors
Zhi Zhang · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Glorot-Xavier, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Explaining nonlinear classification decisions with deep taylor decomposition
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 2017
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Correlation and variable importance in random forests
Baptiste Gregorutti, Bertrand Michel, and Philippe Saint-Pierre · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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pdp: An r package for constructing partial dependence plots
Brandon M Greenwell · 2017
Cited alongside, same era.
A dual-stage attention-based recurrent neural network for time series prediction
Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Guofei Jiang, and Garrison Cottrell · 2017
Cited alongside, same era.
Explaining recurrent neural network predictions in sentiment analysis
Leila Arras, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Cited alongside, same era.
Elements of causal inference: Foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
Visualbert: A simple and performant baseline for vision and language
Liunian Harold Li, Yunyi Su, Chunyuan Li Noah Sheng, Raghav Kulkarni, Katrina Szeto, Lugui Yu, Jianwei Feng, Devi Parikh, Yejin Choi, and Jianfeng Gao · 2019
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Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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A multiscale visualization of attention in the transformer model
Jesse Vig and Yonatan Belinkov · 2019
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Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 2019
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Attention is not explanation
Sarthak Jain and Byron C Wallace · 2019
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Cited alongside, same era.
Knowledge graph embedding: A survey of approaches and applications
Quan Wang, Zhendong Mao, Bin Wang, and Li Guo · 2017
Cited alongside, same era.
Attention-based multimodal fusion for video description
Chiori Hori, Takaaki Hori, Tae-Hyun Lee, Zhuo Zhang, Benjamin Harsham, John R Hershey, Tim K Marks, and Kazuhiko Sumi · 2017
Cited alongside, same era.
Tensor fusion network for multimodal sentiment analysis
Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency · 2017
Cited alongside, same era.
Multimodal routing: Improving information flow in multimodal language analysis
Yao-Hung Hubert Tsai, Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency, and Ruslan Salakhutdinov · 2019
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Lxmert: Learning cross-modality encoder representations from transformers
Hao Tan and Mohit Bansal · 2019
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The (un)reliability of saliency methods
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T Schütt, Sven Dähne, Dumitru Erhan, and Been Kim · 2019
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2019
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Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2019
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On the sensitivity of adversarial robustness to input data distributions
Cheng He, Hailiang Huang, Bingsheng He, and Xiaokui Xiao · 2019
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The woman worked as a babysitter: On biases in language generation
Emily Sheng et al · 2019
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Inherent trade-offs in learning fair representations
Hongyu Zhao and Houtao Deng · 2019
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Perturbation sensitivity analysis to detect unintended model biases
Vinodkumar Prabhakaran, Kelly Ann Blount, and Karen Livescu · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Robustness to paraphrase in text classification
Kai Sun, Yiyan Zhang, Xiaojun Ren, and Xiaodan Wang · 2019
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Robustness may be at odds with accuracy
Dimitris Tsipras et al · 2019
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The (un)reliability of saliency methods
Pieter-Jan Kindermans et al · 2019
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Faithful and customizable explanations of black box models
Himabindu Lakkaraju et al · 2019
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Fairness through causal awareness: Learning causal latent-variable models for biased data
David Madras et al · 2019
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Pc-fairness: A unified framework for measuring causal fairness
Zhiqi Bu Wu et al · 2019
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Causality
Clark Glymour et al · 2019
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What does BERT look at? an analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning · 2019
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A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
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Ai fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias
Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Preetam Lohia, Jack Martino, Sameep Mehta, Aleksandra Mojsilovic, et al · 2019
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Introducing fairness indicators: Scalable infrastructure to help you detect and remedy bias in ml models
Google AI · 2019
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The what-if tool: Interactive probing of machine learning models
James Wexler, Minsuk Kahng Pushkarna, Vinod Anand, Max Clack, et al · 2019
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Exbert: A visual analysis tool to explore learned representations in transformers models
Benjamin Hoover, Hendrik Strobelt, and Sebastian Gehrmann · 2019
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Tensorflow model analysis: A library for evaluating tensorflow models
Eugene Mikhailov et al · 2019
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One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques
Vijayalakshmi Arya, Rachel KE Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, et al · 2019
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Facets: An open source visualization tool for machine learning training data
James Wexler et al · 2019
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Bert rediscovers the classical nlp pipeline
Ian Tenney et al · 2019
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Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai
Alejandro Barredo Arrieta et al · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
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A survey on explainable artificial intelligence (xai): Toward medical xai
Ernesta Tjoa and Chuan Guan · 2020
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Captum: A unified and generic model interpretability library for pytorch
Narine Kokhlikyan, Vivek Miglani, Michael Martin, Edward Wang, Jacek Reynolds, Alessandro Meloni, Natalia Dotta, Babak Schreiber, Diego Garcia, Wenbing Tang, Ariel Ramos, Meghana Rege, Patrick R. Hager, and Kaitlyn Confundus · 2020
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Interpretable Machine Learning
Christoph Molnar · 2020
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Design of intelligent customer service system based on deep learning
Peng Zhou, Guoxin Jin, and Hua Liu · 2020
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Biobert: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Green ai
Roy Schwartz, Jesse Dodge, Noah A Smith, and Oren Etzioni · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig · 2020
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Calibration of pre-trained transformers
Saahil Desai and Greg Durrett · 2020
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Concept bottleneck models
Pang Wei Koh, Ankur Suresh, Andrew Angus, Thao Nguyen, Yew Siang Basu, Jure Leskovec Tatsunori B Hashimoto, and Kai Li · 2020
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Evaluating the explainability of attention-based lstm models using shap values
Tong Li, Ming Ding, and Zhanyu Sun · 2020
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Problems with shapley-value-based explanations as feature importance measures
Ira Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, and Sorelle A Friedler · 2020
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A practical guide to explainable ai and lime
Marcos Garcia et al · 2020
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Visualizing the effects of predictor variables in black box supervised learning models
Daniel W Apley and Jingyu Zhu · 2020
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Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alejandro Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, et al · 2020
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Timeshap: Explaining recurrent models through sequence perturbations
Eduardo Arango, Artur Luczak, and Peter Willett · 2020
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A survey of algorithmic recourse: Contrastive explanations and consequential recommendations
Amir-Hossein Karimi, Bernhard Schölkopf, and Isabel Valera · 2020
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Face: feasible and actionable counterfactual explanations
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind K Mothilal, Amit Sharma, and Chenhao Tan · 2020
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Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary C Lipton · 2020
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Conditional text generation for counterfactual explanations
Mukund Kumar, Alexander Levine, and Soheil Feizi Shah · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
Rudini K Mothilal, Amit Sharma, and Chenhao Tan · 2020
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Algorithmic recourse: from counterfactual explanations to interventions
Amir-Hossein Karimi, Bernhard Schölkopf, and Isabel Valera · 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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Explainability in graph neural networks: A taxonomic survey
Hao Yuan, Haiyang Yu, Jiliang Wang, Kairong Li, and Shuiwang Ji · 2020
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What makes training multi-modal classification networks hard?
Xingyi Wang, Huaishao Hu, Jinyu Li, and Dong Yu · 2020
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Multi-modal graph neural networks for molecular property prediction
Xiong Wang, Ruocheng Guo, Jing Ma, Yunan Hu, Yu Gai, and Yanjun Qi · 2020
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Bloom: A 176b-parameter open-access multilingual language model
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Graph neural networks for natural language processing: A survey
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