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A Unified Approach to Interpreting Model Predictions

22 May 2017

Papers citing "A Unified Approach to Interpreting Model Predictions"

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Title
OncoNetExplainer: Explainable Predictions of Cancer Types Based on Gene Expression Data Md. Rezaul Karim Michael Cochez Oya Beyan Stefan Decker Christoph Lange 15 27 0 09 Sep 2019
Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection Naoya Takeishi FAtt 151 33 0 08 Sep 2019
When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures Gil Fidel Ron Bitton A. Shabtai FAtt GAN 63 120 0 08 Sep 2019
Explainable Deep Learning for Video Recognition Tasks: A Framework & Recommendations Liam Hiley Alun D. Preece Y. Hicks XAI 34 15 0 07 Sep 2019
Generalized Integrated Gradients: A practical method for explaining diverse ensembles John Merrill Geoff Ward S. Kamkar Jay Budzik Douglas C. Merrill 54 15 0 04 Sep 2019
Predicting Consumer Default: A Deep Learning Approach Stefania Albanesi Domonkos F. Vamossy FaML 48 60 0 30 Aug 2019
Human-grounded Evaluations of Explanation Methods for Text Classification Piyawat Lertvittayakumjorn Francesca Toni FAtt 90 67 0 29 Aug 2019
Modeling infection methods of computer malware in the presence of vaccinations using epidemiological models: An analysis of real-world data E. Yom-Tov Nir Levy Amir Rubin 27 7 0 26 Aug 2019
The many Shapley values for model explanation Mukund Sundararajan A. Najmi TDI FAtt 70 645 0 22 Aug 2019
Deep neural network or dermatologist? Kyle Young Gareth Booth B. Simpson R. Dutton Sally Shrapnel MedIm 77 69 0 19 Aug 2019
LoRMIkA: Local rule-based model interpretability with k-optimal associations Dilini Sewwandi Rajapaksha Christoph Bergmeir Wray Buntine 100 31 0 11 Aug 2019
Reconstructing commuters network using machine learning and urban indicators Gabriel Spadon A. Carvalho Jose F. Rodrigues-Jr L. G. Alves HAI 73 68 0 09 Aug 2019
Neural Image Compression and Explanation Xiang Li Shihao Ji 28 10 0 09 Aug 2019
Measurable Counterfactual Local Explanations for Any Classifier Adam White Artur Garcez FAtt 73 98 0 08 Aug 2019
Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance Propagation Brian Kenji Iwana Ryohei Kuroki S. Uchida FAtt 72 98 0 06 Aug 2019
Knowledge Consistency between Neural Networks and Beyond Ruofan Liang Tianlin Li Longfei Li Jingchao Wang Quanshi Zhang 86 28 0 05 Aug 2019
Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations Chanatip Saetia Ekapol Chuangsuwanich Tawunrat Chalothorn P. Vateekul 74 5 0 04 Aug 2019
Supervised and Unsupervised Neural Approaches to Text Readability Matej Martinc Senja Pollak Marko Robnik-Šikonja 99 145 0 26 Jul 2019
How to Manipulate CNNs to Make Them Lie: the GradCAM Case T. Viering Ziqi Wang Marco Loog E. Eisemann AAML FAtt 60 28 0 25 Jul 2019
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations Thibault Laugel Marie-Jeanne Lesot Christophe Marsala X. Renard Marcin Detyniecki 105 198 0 22 Jul 2019
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications Shusen Liu Di Wang D. Maljovec Rushil Anirudh Jayaraman J. Thiagarajan ... Peter B. Robinson H. Bhatia Valerio Pascucci B. Spears P. Bremer 28 11 0 19 Jul 2019
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting Ana Lucic H. Haned Maarten de Rijke 68 64 0 17 Jul 2019
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI Erico Tjoa Cuntai Guan XAI 170 1,466 0 17 Jul 2019
Technical Report: Partial Dependence through Stratification T. Parr James D. Wilson 29 3 0 15 Jul 2019
A study on the Interpretability of Neural Retrieval Models using DeepSHAP Zeon Trevor Fernando Jaspreet Singh Avishek Anand FAtt AAML 65 68 0 15 Jul 2019
Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences Mathias Kraus Stefan Feuerriegel 64 110 0 11 Jul 2019
Explaining an increase in predicted risk for clinical alerts Michaela Hardt A. Rajkomar Gerardo Flores Andrew M. Dai M. Howell Greg S. Corrado Claire Cui Moritz Hardt FAtt 67 12 0 10 Jul 2019
Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models Hilde J. P. Weerts Werner van Ipenburg Mykola Pechenizkiy 43 3 0 07 Jul 2019
A Human-Grounded Evaluation of SHAP for Alert Processing Hilde J. P. Weerts Werner van Ipenburg Mykola Pechenizkiy FAtt 75 70 0 07 Jul 2019
Global Aggregations of Local Explanations for Black Box models I. V. D. Linden H. Haned Evangelos Kanoulas FAtt 71 66 0 05 Jul 2019
On Validating, Repairing and Refining Heuristic ML Explanations Alexey Ignatiev Nina Narodytska Sasha Rubin FAtt LRM 84 63 0 04 Jul 2019
Automating Distributed Tiered Storage Management in Cluster Computing H. Herodotou E. Kakoulli 45 25 0 04 Jul 2019
Consistent Regression using Data-Dependent Coverings Vincent Margot Jean-Patrick Baudry Frédéric Guilloux Olivier Wintenberger 68 5 0 04 Jul 2019
Interpretable Counterfactual Explanations Guided by Prototypes A. V. Looveren Janis Klaise FAtt 116 389 0 03 Jul 2019
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics Niru Maheswaranathan Alex H. Williams Matthew D. Golub Surya Ganguli David Sussillo 81 83 0 25 Jun 2019
Explaining Deep Learning Models with Constrained Adversarial Examples J. Moore Nils Y. Hammerla C. Watkins AAML GAN 67 38 0 25 Jun 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 95 82 0 25 Jun 2019
DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems Muhammad Rehman Zafar N. Khan FAtt 124 159 0 24 Jun 2019
Generating Counterfactual and Contrastive Explanations using SHAP Shubham Rathi 77 57 0 21 Jun 2019
Incorporating Priors with Feature Attribution on Text Classification Frederick Liu Besim Avci FAtt FaML 108 120 0 19 Jun 2019
Explanations can be manipulated and geometry is to blame Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders M. Ackermann K. Müller Pan Kessel AAML FAtt 92 336 0 19 Jun 2019
VizADS-B: Analyzing Sequences of ADS-B Images Using Explainable Convolutional LSTM Encoder-Decoder to Detect Cyber Attacks Sefi Akerman Edan Habler A. Shabtai 82 18 0 19 Jun 2019
From Clustering to Cluster Explanations via Neural Networks Jacob R. Kauffmann Malte Esders Lukas Ruff G. Montavon Wojciech Samek K. Müller 79 72 0 18 Jun 2019
ASAC: Active Sensing using Actor-Critic models Jinsung Yoon James Jordon M. Schaar CML 59 16 0 16 Jun 2019
Understanding artificial intelligence ethics and safety David Leslie FaML AI4TS 74 363 0 11 Jun 2019
Issues with post-hoc counterfactual explanations: a discussion Thibault Laugel Marie-Jeanne Lesot Christophe Marsala Marcin Detyniecki CML 139 46 0 11 Jun 2019
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding Haotian Ma Hao Zhang Fan Zhou Yinqing Zhang Quanshi Zhang FAtt 25 0 0 10 Jun 2019
Proposed Guidelines for the Responsible Use of Explainable Machine Learning Patrick Hall Navdeep Gill N. Schmidt SILM XAI FaML 80 29 0 08 Jun 2019
ML-LOO: Detecting Adversarial Examples with Feature Attribution Puyudi Yang Jianbo Chen Cho-Jui Hsieh Jane-ling Wang Michael I. Jordan AAML 93 101 0 08 Jun 2019
Evaluating Explanation Methods for Deep Learning in Security Alexander Warnecke Dan Arp Christian Wressnegger Konrad Rieck XAI AAML FAtt 71 94 0 05 Jun 2019