v1v2v3v4 (latest)

Interpretations are useful: penalizing explanations to align neural networks with prior knowledge

30 September 2019

Papers citing "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge"

41 / 41 papers shown

Title
Explanation Regularisation through the Lens of Attributions Pedro Ferreira Wilker Aziz Ivan Titov 131 1 0 23 Jul 2024
Identifying Spurious Correlations using Counterfactual Alignment Joseph Paul Cohen Louis Blankemeier Akshay S. Chaudhari CML 96 1 0 01 Dec 2023
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation Karan Goel Albert Gu Yixuan Li Christopher Ré 84 121 0 15 Aug 2020
Transformation Importance with Applications to Cosmology Chandan Singh Wooseok Ha F. Lanusse V. Boehm Jia-Wei Liu Bin Yu AI4CE 52 11 0 04 Mar 2020
Learning Credible Deep Neural Networks with Rationale Regularization Mengnan Du Ninghao Liu Fan Yang Xia Hu FaML 82 46 0 13 Aug 2019
Improving performance of deep learning models with axiomatic attribution priors and expected gradients G. Erion Joseph D. Janizek Pascal Sturmfels Scott M. Lundberg Su-In Lee OOD BDL FAtt 67 82 0 25 Jun 2019
Incorporating Priors with Feature Attribution on Text Classification Frederick Liu Besim Avci FAtt FaML 93 120 0 19 Jun 2019
Do Human Rationales Improve Machine Explanations? Julia Strout Ye Zhang Raymond J. Mooney 63 58 0 31 May 2019
Interpreting Adversarially Trained Convolutional Neural Networks Tianyuan Zhang Zhanxing Zhu AAML GAN FAtt 102 161 0 23 May 2019
Embedding Human Knowledge into Deep Neural Network via Attention Map Masahiro Mitsuhara Hiroshi Fukui Yusuke Sakashita Takanori Ogata Tsubasa Hirakawa Takayoshi Yamashita H. Fujiyoshi 59 73 0 09 May 2019
REPAIR: Removing Representation Bias by Dataset Resampling Yi Li Nuno Vasconcelos FaML 79 287 0 16 Apr 2019
Learning Robust Representations by Projecting Superficial Statistics Out Haohan Wang Zexue He Zachary Chase Lipton Eric Xing OOD 73 235 0 02 Mar 2019
Aggregating explanation methods for stable and robust explainability Laura Rieger Lars Kai Hansen AAML FAtt 57 12 0 01 Mar 2019
Attention is not Explanation Sarthak Jain Byron C. Wallace FAtt 148 1,328 0 26 Feb 2019
Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) Noel Codella V. Rotemberg P. Tschandl M. E. Celebi Stephen W. Dusza ... Aadi Kalloo Konstantinos Liopyris Michael Marchetti Harald Kittler Allan Halpern 110 1,191 0 09 Feb 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin Yu XAI HAI 198 1,444 0 14 Jan 2019
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness Robert Geirhos Patricia Rubisch Claudio Michaelis Matthias Bethge Felix Wichmann Wieland Brendel 124 2,673 0 29 Nov 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 141 1,969 0 08 Oct 2018
Deriving Machine Attention from Human Rationales Yujia Bao Shiyu Chang Mo Yu Regina Barzilay 33 107 0 28 Aug 2018
Women also Snowboard: Overcoming Bias in Captioning Models (Extended Abstract) Lisa Anne Hendricks Kaylee Burns Kate Saenko Trevor Darrell Anna Rohrbach 121 480 0 02 Jul 2018
Hierarchical interpretations for neural network predictions Chandan Singh W. James Murdoch Bin Yu 68 146 0 14 Jun 2018
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations Weili Nie Yang Zhang Ankit B. Patel FAtt 127 151 0 18 May 2018
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs W. James Murdoch Peter J. Liu Bin Yu 80 210 0 16 Jan 2018
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients A. Ross Finale Doshi-Velez AAML 154 683 0 26 Nov 2017
Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes Nikhil Garg L. Schiebinger Dan Jurafsky James Zou AI4TS 71 965 0 22 Nov 2017
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer David Madras T. Pitassi R. Zemel FaML 163 221 0 17 Nov 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,002 0 22 May 2017
Real Time Image Saliency for Black Box Classifiers P. Dabkowski Y. Gal 70 592 0 22 May 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 76 1,525 0 11 Apr 2017
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations A. Ross M. C. Hughes Finale Doshi-Velez FAtt 129 591 0 10 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 193 6,018 0 04 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 405 3,813 0 28 Feb 2017
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis L. Zintgraf Taco S. Cohen T. Adel Max Welling FAtt 143 708 0 15 Feb 2017
Automatic Rule Extraction from Long Short Term Memory Networks W. James Murdoch Arthur Szlam 58 87 0 08 Feb 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 321 20,086 0 07 Oct 2016
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings Tolga Bolukbasi Kai-Wei Chang James Zou Venkatesh Saligrama Adam Kalai CVBM FaML 112 3,150 0 21 Jul 2016
Not Just a Black Box: Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Shcherbina A. Kundaje FAtt 85 791 0 05 May 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,027 0 16 Feb 2016
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 251 4,681 0 21 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.7K 100,508 0 04 Sep 2014
How to Explain Individual Classification Decisions D. Baehrens T. Schroeter Stefan Harmeling M. Kawanabe K. Hansen K. Müller FAtt 137 1,104 0 06 Dec 2009