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"Why Should I Trust You?": Explaining the Predictions of Any Classifier

16 February 2016

Papers citing ""Why Should I Trust You?": Explaining the Predictions of Any Classifier"

50 / 4,966 papers shown

Title
Distilling a Neural Network Into a Soft Decision Tree Nicholas Frosst Geoffrey E. Hinton 439 639 0 27 Nov 2017
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients A. Ross Finale Doshi-Velez AAML 159 686 0 26 Nov 2017
The Promise and Peril of Human Evaluation for Model Interpretability Bernease Herman 74 144 0 20 Nov 2017
How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games Jonathan Dodge Sean Penney Claudia Hilderbrand Andrew Anderson Margaret Burnett 39 34 0 19 Nov 2017
Excitation Backprop for RNNs Sarah Adel Bargal Andrea Zunino Donghyun Kim Jianming Zhang Vittorio Murino Stan Sclaroff 166 48 0 18 Nov 2017
Improving Palliative Care with Deep Learning Anand Avati Kenneth Jung S. Harman L. Downing A. Ng N. Shah 146 375 0 17 Nov 2017
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability Mike Wu M. C. Hughes S. Parbhoo Maurizio Zazzi Volker Roth Finale Doshi-Velez AI4CE 143 283 0 16 Nov 2017
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis Rushil Anirudh Jayaraman J. Thiagarajan R. Sridhar T. Bremer FAtt AAML 58 12 0 15 Nov 2017
Towards Interpretable R-CNN by Unfolding Latent Structures Tianfu Wu Wei Sun Xilai Li Xi Song Yangqiu Song ObjD 62 20 0 14 Nov 2017
Dynamic Analysis of Executables to Detect and Characterize Malware Michael R. Smith J. Ingram Christopher C. Lamb T. Draelos J. Doak J. Aimone C. James 42 13 0 10 Nov 2017
Learning Credible Models Jiaxuan Wang Jeeheh Oh Haozhu Wang Jenna Wiens FaML 87 30 0 08 Nov 2017
"Dave...I can assure you...that it's going to be all right..." -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships Brett W. Israelsen Nisar R. Ahmed 70 86 0 08 Nov 2017
Distributed Bayesian Piecewise Sparse Linear Models M. Asahara R. Fujimaki 19 0 0 07 Nov 2017
Visualizing and Understanding Atari Agents S. Greydanus Anurag Koul Jonathan Dodge Alan Fern FAtt 133 348 0 31 Oct 2017
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks Aditya Chattopadhyay Anirban Sarkar Prantik Howlader V. Balasubramanian FAtt 144 2,319 0 30 Oct 2017
Understanding Hidden Memories of Recurrent Neural Networks Yao Ming Shaozu Cao Ruixiang Zhang Zerui Li Yuanzhe Chen Yangqiu Song Huamin Qu HAI 48 201 0 30 Oct 2017
Examining CNN Representations with respect to Dataset Bias Quanshi Zhang Wenguan Wang Song-Chun Zhu SSL FAtt 61 104 0 29 Oct 2017
Do Convolutional Neural Networks Learn Class Hierarchy? B. Alsallakh Amin Jourabloo Mao Ye Xiaoming Liu Liu Ren 186 215 0 17 Oct 2017
Interpretable Convolutional Neural Networks Quanshi Zhang Ying Nian Wu Song-Chun Zhu FAtt 100 784 0 02 Oct 2017
Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks Yuki Saito Shinnosuke Takamichi Hiroshi Saruwatari 76 199 0 23 Sep 2017
Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward Ramnath Kumar Andrew W. Wicker Matt Swann AAML 43 43 0 20 Sep 2017
Human Understandable Explanation Extraction for Black-box Classification Models Based on Matrix Factorization Jaedeok Kim Ji-Hoon Seo FAtt 101 8 0 18 Sep 2017
Embedding Deep Networks into Visual Explanations Zhongang Qi Saeed Khorram Fuxin Li 41 27 0 15 Sep 2017
Learning Functional Causal Models with Generative Neural Networks Hugo Jair Escalante Sergio Escalera Xavier Baro Isabelle M Guyon Umut Güçlü Marcel van Gerven CML BDL 105 108 0 15 Sep 2017
Interpreting Shared Deep Learning Models via Explicable Boundary Trees Huijun Wu Chen Wang Jie Yin Kai Lu Liming Zhu FedML 36 5 0 12 Sep 2017
Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction Devinder Kumar Graham W. Taylor Alexander Wong AIFin 50 18 0 05 Sep 2017
Learning the PE Header, Malware Detection with Minimal Domain Knowledge Edward Raff Jared Sylvester Charles K. Nicholas 83 119 0 05 Sep 2017
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models Wojciech Samek Thomas Wiegand K. Müller XAI VLM 95 1,195 0 28 Aug 2017
Understanding and Comparing Deep Neural Networks for Age and Gender Classification Sebastian Lapuschkin Alexander Binder K. Müller Wojciech Samek CVBM 94 135 0 25 Aug 2017
Explaining Anomalies in Groups with Characterizing Subspace Rules Meghanath Macha Leman Akoglu 43 39 0 20 Aug 2017
Early Stage Malware Prediction Using Recurrent Neural Networks Matilda Rhode Pete Burnap K. Jones AAML 72 255 0 11 Aug 2017
Data-driven Advice for Applying Machine Learning to Bioinformatics Problems Randal S. Olson William La Cava Zairah Mustahsan Akshay Varik J. Moore OOD 69 266 0 08 Aug 2017
Axiomatic Characterization of Data-Driven Influence Measures for Classification Jakub Sliwinski Martin Strobel Yair Zick TDI 62 14 0 07 Aug 2017
Machine learning for neural decoding Joshua I. Glaser Ari S. Benjamin Raeed H. Chowdhury M. Perich L. Miller Konrad Paul Kording 107 248 0 02 Aug 2017
Interpretable Active Learning R. L. Phillips K. H. Chang Sorelle A. Friedler FAtt 52 28 0 31 Jul 2017
Analysis and Optimization of Convolutional Neural Network Architectures Martin Thoma 99 73 0 31 Jul 2017
Using Program Induction to Interpret Transition System Dynamics Svetlin Penkov S. Ramamoorthy AI4CE 66 11 0 26 Jul 2017
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy? Lin Chen Moran Feldman Amin Karbasi 77 47 0 13 Jul 2017
A Formal Framework to Characterize Interpretability of Procedures Amit Dhurandhar Vijay Iyengar Ronny Luss Karthikeyan Shanmugam 47 19 0 12 Jul 2017
Efficient mixture model for clustering of sparse high dimensional binary data Marek Śmieja Krzysztof Hajto Jacek Tabor 27 15 0 11 Jul 2017
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 368 205 0 06 Jul 2017
Efficient Data Representation by Selecting Prototypes with Importance Weights Karthik S. Gurumoorthy Amit Dhurandhar Guillermo Cecchi Charu Aggarwal 97 22 0 05 Jul 2017
Interpretable & Explorable Approximations of Black Box Models Himabindu Lakkaraju Ece Kamar R. Caruana J. Leskovec FAtt 95 254 0 04 Jul 2017
Interpretability via Model Extraction Osbert Bastani Carolyn Kim Hamsa Bastani FAtt 78 129 0 29 Jun 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 296 2,275 0 24 Jun 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 264 4,293 0 22 Jun 2017
Explaining Recurrent Neural Network Predictions in Sentiment Analysis L. Arras G. Montavon K. Müller Wojciech Samek FAtt 110 354 0 22 Jun 2017
MAGIX: Model Agnostic Globally Interpretable Explanations Nikaash Puri Piyush B. Gupta Pratiksha Agarwal Sukriti Verma Balaji Krishnamurthy FAtt 111 41 0 22 Jun 2017
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking Gabriele Tolomei Fabrizio Silvestri Andrew Haines M. Lalmas 77 209 0 20 Jun 2017
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models Garrett B. Goh Charles Siegel Abhinav Vishnu Nathan Oken Hodas Nathan Baker 103 158 0 20 Jun 2017