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Axiomatic Attribution for Deep Networks

4 March 2017

Ankur Taly

Papers citing "Axiomatic Attribution for Deep Networks"

50 / 2,872 papers shown

Title
Deep metric learning improves lab of origin prediction of genetically engineered plasmids I. M. Soares Fernando H. F. Camargo Adriano Marques Oliver M. Crook 16 0 0 24 Nov 2021
A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binaries P. Dasgupta Zachary Osman AAML 66 2 0 22 Nov 2021
Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors Ruoxi Sun Minhui Xue Gareth Tyson Tian Dong Shaofeng Li Shuo Wang Haojin Zhu S. Çamtepe Surya Nepal AAML 116 16 0 19 Nov 2021
Acquisition of Chess Knowledge in AlphaZero Thomas McGrath A. Kapishnikov Nenad Tomašev Adam Pearce Demis Hassabis Been Kim Ulrich Paquet Vladimir Kramnik 77 169 0 17 Nov 2021
STEEX: Steering Counterfactual Explanations with Semantics P. Jacob Éloi Zablocki H. Ben-younes Mickaël Chen P. Pérez Matthieu Cord 79 43 0 17 Nov 2021
Selective Ensembles for Consistent Predictions Emily Black Klas Leino Matt Fredrikson 71 23 0 16 Nov 2021
A Survey on Adversarial Attacks for Malware Analysis Kshitiz Aryal Maanak Gupta Mahmoud Abdelsalam AAML 114 53 0 16 Nov 2021
Fast Axiomatic Attribution for Neural Networks Robin Hesse Simone Schaub-Meyer Stefan Roth 53 40 0 15 Nov 2021
Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks L. Espeholt Shreya Agrawal C. Sønderby M. Kumar Jonathan Heek Carla Bromberg Cenk Gazen Jason Hickey Aaron Bell Nal Kalchbrenner AI4Cl 76 48 0 14 Nov 2021
A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines V. Borisov Johannes Meier J. V. D. Heuvel Hamed Jalali Gjergji Kasneci FAtt 72 5 0 14 Nov 2021
"Will You Find These Shortcuts?" A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification Jasmijn Bastings Sebastian Ebert Polina Zablotskaia Anders Sandholm Katja Filippova 152 78 0 14 Nov 2021
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications Adrien Bennetot Ivan Donadello Ayoub El Qadi M. Dragoni Thomas Frossard ... M. Trocan Raja Chatila Andreas Holzinger Artur Garcez Natalia Díaz Rodríguez XAI 68 9 0 13 Nov 2021
Discovering and Explaining the Representation Bottleneck of DNNs Huiqi Deng Qihan Ren Hao Zhang Quanshi Zhang 124 61 0 11 Nov 2021
Defining and Quantifying the Emergence of Sparse Concepts in DNNs Jie Ren Mingjie Li Qirui Chen Huiqi Deng Quanshi Zhang 135 33 0 11 Nov 2021
Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics Amirata Ghorbani Dina Berenbaum Maor Ivgi Yuval Dafna James Zou FAtt 54 9 0 10 Nov 2021
Data-Driven AI Model Signal-Awareness Enhancement and Introspection Sahil Suneja Yufan Zhuang Yunhui Zheng Jim Laredo Alessandro Morari SyDa 105 1 0 10 Nov 2021
Counterfactual Explanations for Models of Code Jürgen Cito Işıl Dillig V. Murali S. Chandra AAML LRM 73 52 0 10 Nov 2021
Can Information Flows Suggest Targets for Interventions in Neural Circuits? Praveen Venkatesh Sanghamitra Dutta Neil Mehta P. Grover AAML 75 8 0 09 Nov 2021
Self-Interpretable Model with TransformationEquivariant Interpretation Yipei Wang Xiaoqian Wang 77 23 0 09 Nov 2021
Defense Against Explanation Manipulation Ruixiang Tang Ninghao Liu Fan Yang Na Zou Helen Zhou AAML 91 12 0 08 Nov 2021
Natural Adversarial Objects Felix Lau Nishant Subramani Sasha Harrison Aerin Kim E. Branson Rosanne Liu 100 7 0 07 Nov 2021
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis Thomas Fel Rémi Cadène Mathieu Chalvidal Matthieu Cord David Vigouroux Thomas Serre MLAU FAtt AAML 173 65 0 07 Nov 2021
Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach Mao Guan Xiao-Yang Liu AIFin AI4TS 51 21 0 07 Nov 2021
"How Does It Detect A Malicious App?" Explaining the Predictions of AI-based Android Malware Detector Zhi Lu V. Thing AAML 46 4 0 06 Nov 2021
Explaining neural network predictions of material strength Ian Palmer T. Nathan Mundhenk B. J. Gallagher Yong Han 53 2 0 05 Nov 2021
Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions Huy P Phan Kaare B. Mikkelsen 81 96 0 03 Nov 2021
On the Effectiveness of Interpretable Feedforward Neural Network Miles Q. Li Benjamin C. M. Fung Adel Abusitta FaML AI4CE 37 4 0 03 Nov 2021
Human Attention in Fine-grained Classification Yao Rong Wenjia Xu Zeynep Akata Enkelejda Kasneci 98 37 0 02 Nov 2021
Gradient Frequency Modulation for Visually Explaining Video Understanding Models Xinmiao Lin Wentao Bao Matthew Wright Yu Kong FAtt AAML 83 2 0 01 Nov 2021
Provably efficient, succinct, and precise explanations Guy Blanc Jane Lange Li-Yang Tan FAtt 136 38 0 01 Nov 2021
Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods Zohaib Salahuddin Henry C. Woodruff A. Chatterjee Philippe Lambin 90 322 0 01 Nov 2021
A Survey on the Robustness of Feature Importance and Counterfactual Explanations Saumitra Mishra Sanghamitra Dutta Jason Long Daniele Magazzeni AAML 86 58 0 30 Oct 2021
Explaining Latent Representations with a Corpus of Examples Jonathan Crabbé Zhaozhi Qian F. Imrie M. Schaar FAtt 84 38 0 28 Oct 2021
Separating Content and Style for Unsupervised Image-to-Image Translation Yunfei Liu Haofei Wang Yang Yue Feng Lu 71 3 0 27 Oct 2021
Revisiting Sanity Checks for Saliency Maps G. Yona D. Greenfeld AAML FAtt 56 25 0 27 Oct 2021
Understanding Interlocking Dynamics of Cooperative Rationalization Mo Yu Yang Zhang Shiyu Chang Tommi Jaakkola 94 44 0 26 Oct 2021
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set Gabriel Laberge Y. Pequignot Alexandre Mathieu Foutse Khomh M. Marchand FAtt 75 6 0 26 Oct 2021
ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AI Samuel Hess G. Ditzler AAML 89 1 0 22 Oct 2021
Interpreting Deep Learning Models in Natural Language Processing: A Review Xiaofei Sun Diyi Yang Xiaoya Li Tianwei Zhang Yuxian Meng Han Qiu Guoyin Wang Eduard H. Hovy Jiwei Li 99 47 0 20 Oct 2021
Multi-concept adversarial attacks Vibha Belavadi Yan Zhou Murat Kantarcioglu B. Thuraisingham AAML 85 0 0 19 Oct 2021
Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learning Bang Xiang Yong Alexandra Brintrup 61 7 0 19 Oct 2021
Schrödinger's Tree -- On Syntax and Neural Language Models Artur Kulmizev Joakim Nivre 77 6 0 17 Oct 2021
TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models S. Chatterjee Arnab Das Chirag Mandal Budhaditya Mukhopadhyay Manish Vipinraj Aniruddh Shukla R. Rao Chompunuch Sarasaen Oliver Speck A. Nürnberger MedIm 78 15 0 16 Oct 2021
Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining Andreas Madsen Nicholas Meade Vaibhav Adlakha Siva Reddy 157 37 0 15 Oct 2021
Robustness of different loss functions and their impact on networks learning capability Vishal Rajput OOD AAML 28 14 0 15 Oct 2021
NeuroView: Explainable Deep Network Decision Making C. Barberan Randall Balestriero Richard G. Baraniuk FAtt 54 2 0 15 Oct 2021
Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models Tianlu Wang Rohit Sridhar Diyi Yang Xuezhi Wang AAML 215 77 0 14 Oct 2021
Making Document-Level Information Extraction Right for the Right Reasons Liyan Tang Dhruv Rajan S. Mohan Abhijeet Pradhan R. Bryan Greg Durrett 34 1 0 14 Oct 2021
Interactive Analysis of CNN Robustness Stefan Sietzen Mathias Lechner Judy Borowski Ramin Hasani Manuela Waldner AAML 81 10 0 14 Oct 2021
Can Explanations Be Useful for Calibrating Black Box Models? Xi Ye Greg Durrett FAtt 70 25 0 14 Oct 2021