SmoothGrad: removing noise by adding noise

12 June 2017

Papers citing "SmoothGrad: removing noise by adding noise"

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Title
Neural Network Attributions: A Causal Perspective Aditya Chattopadhyay Piyushi Manupriya Anirban Sarkar V. Balasubramanian CML 11 143 0 06 Feb 2019
Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo Sunghwan Joo Taesup Moon AAML FAtt 18 201 0 06 Feb 2019
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation Sahil Singla Eric Wallace Shi Feng S. Feizi FAtt 18 59 0 01 Feb 2019
An Evaluation of the Human-Interpretability of Explanation Isaac Lage Emily Chen Jeffrey He Menaka Narayanan Been Kim Sam Gershman Finale Doshi-Velez FAtt XAI 13 151 0 31 Jan 2019
Interpreting Deep Neural Networks Through Variable Importance J. Ish-Horowicz Dana Udwin Seth Flaxman Sarah Filippi Lorin Crawford FAtt 14 13 0 28 Jan 2019
On the (In)fidelity and Sensitivity for Explanations Chih-Kuan Yeh Cheng-Yu Hsieh A. Suggala David I. Inouye Pradeep Ravikumar FAtt 39 448 0 27 Jan 2019
Toward Explainable Fashion Recommendation Pongsate Tangseng Takayuki Okatani 21 29 0 15 Jan 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin-Xia Yu XAI HAI 47 1,417 0 14 Jan 2019
Attention Branch Network: Learning of Attention Mechanism for Visual Explanation Hiroshi Fukui Tsubasa Hirakawa Takayoshi Yamashita H. Fujiyoshi XAI FAtt 11 399 0 25 Dec 2018
AVRA: Automatic Visual Ratings of Atrophy from MRI images using Recurrent Convolutional Neural Networks G. Mårtensson D. Ferreira L. Cavallin J.-Sebastian Muehlboeck L. Wahlund Chunliang Wang E. Westman 33 20 0 23 Dec 2018
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability Xiaowei Huang Daniel Kroening Wenjie Ruan Marta Kwiatkowska Youcheng Sun Emese Thamo Min Wu Xinping Yi AAML 24 50 0 18 Dec 2018
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection David Zimmerer Simon A. A. Kohl Jens Petersen Fabian Isensee Klaus H. Maier-Hein DRL 19 128 0 14 Dec 2018
Can I trust you more? Model-Agnostic Hierarchical Explanations Michael Tsang Youbang Sun Dongxu Ren Yan Liu FAtt 16 25 0 12 Dec 2018
Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics Biye Jiang David M. Chan Tianhao Zhang John F. Canny FAtt 17 0 0 11 Dec 2018
Interpretable Deep Learning under Fire Xinyang Zhang Ningfei Wang Hua Shen S. Ji Xiapu Luo Ting Wang AAML AI4CE 22 169 0 03 Dec 2018
Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks Phillip E. Pope Soheil Kolouri Mohammad Rostami Charles E. Martin Heiko Hoffmann GNN 38 14 0 01 Dec 2018
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 191 1,032 0 29 Nov 2018
Representer Point Selection for Explaining Deep Neural Networks Chih-Kuan Yeh Joon Sik Kim Ian En-Hsu Yen Pradeep Ravikumar TDI 6 243 0 23 Nov 2018
On a Sparse Shortcut Topology of Artificial Neural Networks Fenglei Fan Dayang Wang Hengtao Guo Qikui Zhu Pingkun Yan Ge Wang Hengyong Yu 38 22 0 22 Nov 2018
Bioresorbable Scaffold Visualization in IVOCT Images Using CNNs and Weakly Supervised Localization N. Gessert S. Latus Youssef S. Abdelwahed D. Leistner Matthias Lutz Alexander Schlaefer 11 6 0 22 Oct 2018
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values Julius Adebayo Justin Gilmer Ian Goodfellow Been Kim FAtt AAML 19 128 0 08 Oct 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 64 1,928 0 08 Oct 2018
Interpreting Layered Neural Networks via Hierarchical Modular Representation C. Watanabe 21 19 0 03 Oct 2018
Training Machine Learning Models by Regularizing their Explanations A. Ross FaML 26 0 0 29 Sep 2018
Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability Zhihao Li Toshiyuki Motoyoshi Kazuma Sasaki T. Ogata S. Sugano LRM 16 39 0 28 Sep 2018
Interpreting Neural Networks With Nearest Neighbors Eric Wallace Shi Feng Jordan L. Boyd-Graber AAML FAtt MILM 15 53 0 08 Sep 2018
iNNvestigate neural networks! Maximilian Alber Sebastian Lapuschkin P. Seegerer Miriam Hagele Kristof T. Schütt G. Montavon Wojciech Samek K. Müller Sven Dähne Pieter-Jan Kindermans 24 348 0 13 Aug 2018
Techniques for Interpretable Machine Learning Mengnan Du Ninghao Liu Xia Hu FaML 39 1,072 0 31 Jul 2018
Model Agnostic Saliency for Weakly Supervised Lesion Detection from Breast DCE-MRI Gabriel Maicas G. Snaauw A. Bradley Ian Reid G. Carneiro MedIm 11 14 0 20 Jul 2018
Model Reconstruction from Model Explanations S. Milli Ludwig Schmidt Anca Dragan Moritz Hardt FAtt 21 177 0 13 Jul 2018
Direct Uncertainty Prediction for Medical Second Opinions M. Raghu Katy Blumer Rory Sayres Ziad Obermeyer Robert D. Kleinberg S. Mullainathan Jon M. Kleinberg OOD UD 27 136 0 04 Jul 2018
BayesGrad: Explaining Predictions of Graph Convolutional Networks Hirotaka Akita Kosuke Nakago Tomoki Komatsu Yohei Sugawara S. Maeda Yukino Baba H. Kashima FAtt OOD BDL 19 8 0 04 Jul 2018
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker D. Erhan Pieter-Jan Kindermans Been Kim FAtt UQCV 31 670 0 28 Jun 2018
This Looks Like That: Deep Learning for Interpretable Image Recognition Chaofan Chen Oscar Li Chaofan Tao A. Barnett Jonathan Su Cynthia Rudin 59 1,157 0 27 Jun 2018
xGEMs: Generating Examplars to Explain Black-Box Models Shalmali Joshi Oluwasanmi Koyejo Been Kim Joydeep Ghosh MLAU 25 40 0 22 Jun 2018
Maximally Invariant Data Perturbation as Explanation Satoshi Hara Kouichi Ikeno Tasuku Soma Takanori Maehara AAML 14 8 0 19 Jun 2018
Hierarchical interpretations for neural network predictions Chandan Singh W. James Murdoch Bin Yu 31 145 0 14 Jun 2018
Producing radiologist-quality reports for interpretable artificial intelligence William Gale Luke Oakden-Rayner G. Carneiro A. Bradley L. Palmer MedIm 19 46 0 01 Jun 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 40 1,842 0 31 May 2018
Semantic Network Interpretation Pei Guo Ryan Farrell MILM FAtt 15 0 0 23 May 2018
Learning what and where to attend Drew Linsley Dan Scheibler S. Eberhardt Thomas Serre 22 32 0 22 May 2018
Generalizing multistain immunohistochemistry tissue segmentation using one-shot color deconvolution deep neural networks Amal Lahiani J. Gildenblat I. Klaman Nassir Navab Eldad Klaiman 13 15 0 17 May 2018
Pathologies of Neural Models Make Interpretations Difficult Shi Feng Eric Wallace Alvin Grissom II Mohit Iyyer Pedro Rodriguez Jordan L. Boyd-Graber AAML FAtt 13 317 0 20 Apr 2018
Single Day Outdoor Photometric Stereo Yannick Hold-Geoffroy Paulo F. U. Gotardo Jean-François Lalonde 25 15 0 28 Mar 2018
Classification of crystallization outcomes using deep convolutional neural networks Andrew E. Bruno P. Charbonneau J. Newman E. Snell David R. So Vincent Vanhoucke Christopher J. Watkins Shawn Williams Julie Wilson 13 67 0 27 Mar 2018
Towards Explanation of DNN-based Prediction with Guided Feature Inversion Mengnan Du Ninghao Liu Qingquan Song Xia Hu FAtt 12 125 0 19 Mar 2018
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge Kai Xu Dae Hoon Park Chang Yi Charles Sutton HAI FAtt 14 26 0 11 Mar 2018
Understanding and Enhancing the Transferability of Adversarial Examples Lei Wu Zhanxing Zhu Cheng Tai E. Weinan AAML SILM 30 96 0 27 Feb 2018
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution Lingyang Chu X. Hu Juhua Hu Lanjun Wang J. Pei 12 99 0 17 Feb 2018
Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks Patrick Schwab Djordje Miladinovic W. Karlen CML 21 57 0 06 Feb 2018