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Interpretability methods are valuable only if their explanations faithfully describe the explained model.
Probability inequalities for sums of bounded random variables
Wassily Hoeffding · 1963
Earlier work this paper cites.
Bayesian estimates of equation system parameters: An application of integration by monte carlo
T. Kloek and H. K. van Dijk · 1978
Earlier work this paper cites.
A Course in the Theory of Groups
D. Robinson, F.W. Gehring, and P.R. Halmos · 1996
Earlier work this paper cites.
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals
A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, and H. E. Stanley · 2000
Earlier work this paper cites.
The impact of the mit-bih arrhythmia database
G.B. Moody and R.G. Mark · 2001
Earlier work this paper cites.
Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
Earlier work this paper cites.
Derivation and validation of toxicophores for mutagenicity prediction
Jeroen Kazius, Ross McGuire, and Roberta Bursi · 2005
Earlier work this paper cites.
Iam graph database repository for graph based pattern recognition and machine learning
Kaspar Riesen and Horst Bunke · 2008
Earlier work this paper cites.
Exploring network structure, dynamics, and function using networkx
Aric A. Hagberg, Daniel A. Schult, and Pieter J. Swart · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Python 3 Reference Manual
Guido Van Rossum and Fred L. Drake · 2009
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
The bayesian case model: A generative approach for case-based reasoning and prototype classification
Been Kim, Cynthia Rudin, and Julie A Shah · 2014
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
"why should i trust you?": Explaining the predictions of any classifier
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Earlier work this paper cites.
Deep residual learning for image recognition
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Earlier work this paper cites.
Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
Earlier work this paper cites.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Earlier work this paper cites.
Attention is all you need
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Earlier work this paper cites.
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Finale Doshi-Velez and Been Kim · 2017
Earlier work this paper cites.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Earlier work this paper cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Earlier work this paper cites.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Earlier work this paper cites.
Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
Earlier work this paper cites.
Deep sets
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Earlier work this paper cites.
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Evaluating robustness of neural networks with mixed integer programming
Vincent Tjeng, Kai Xiao, and Russ Tedrake · 2017
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Deepsafe: A data-driven approach for checking adversarial robustness in neural networks
Divya Gopinath, Guy Katz, Corina S Pasareanu, and Clark Barrett · 2017
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Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Estimating training data influence by tracing gradient descent
Garima, Frederick Liu, Satyen Kale, and Mukund Sundararajan · 2020
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Robustness in machine learning explanations: Does it matter?
Leif Hancox-Li · 2020
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Evaluating and aggregating feature-based model explanations
Umang Bhatt, Adrian Weller, and José MF Moura · 2020
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Smoothed geometry for robust attribution
Zifan Wang, Haofan Wang, Shakul Ramkumar, Piotr Mardziel, Matt Fredrikson, and Anupam Datta · 2020
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
Later among the works it cites.
Tudataset: A collection of benchmark datasets for learning with graphs
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Opportunities and obstacles for deep learning in biology and medicine
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 2018
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On the robustness of interpretability methods
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Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Learning translation invariance in cnns
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Visualizing the impact of feature attribution baselines
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Captum: A unified and generic model interpretability library for pytorch, 2020
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Transformers are graph neural networks
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Highly accurate protein structure prediction with alphafold
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Explaining time series predictions with dynamic masks
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Explaining latent representations with a corpus of examples
Jonathan Crabbe, Zhaozhi Qian, Fergus Imrie, and Mihaela van der Schaar · 2021
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Evaluating the quality of machine learning explanations: A survey on methods and metrics
Jianlong Zhou, Amir H Gandomi, Fang Chen, and Andreas Holzinger · 2021
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Self-interpretable model with transformation equivariant interpretation
Yipei Wang and Xiaoqian Wang · 2021
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
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Improving performance of deep learning models with axiomatic attribution priors and expected gradients
Gabriel Erion, Joseph D Janizek, Pascal Sturmfels, Scott M Lundberg, and Su-In Lee · 2021
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E(n) equivariant graph neural networks
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Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks
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Concept activation regions: A generalized framework for concept-based explanations
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Interpretation quality score for measuring the quality of interpretability methods
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Robust explainability: A tutorial on gradient-based attribution methods for deep neural networks
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Label-free explainability for unsupervised models
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Contrastive corpus attribution for explaining representations
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Machine learning testing: Survey, landscapes and horizons
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E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
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