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Axiomatic Attribution for Deep Networks

4 March 2017

Ankur Taly

Papers citing "Axiomatic Attribution for Deep Networks"

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Title
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning Zhaomin Wu Qinbin Li Bingsheng He FedML 62 20 0 11 Jun 2021
Explaining Time Series Predictions with Dynamic Masks Jonathan Crabbé M. Schaar FAtt AI4TS 104 81 0 09 Jun 2021
On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluation Wei Zhang Ziming Huang Yada Zhu Guangnan Ye Xiaodong Cui Fan Zhang 123 17 0 09 Jun 2021
On the Lack of Robust Interpretability of Neural Text Classifiers Muhammad Bilal Zafar Michele Donini Dylan Slack Cédric Archambeau Sanjiv Ranjan Das K. Kenthapadi AAML 68 21 0 08 Jun 2021
3DB: A Framework for Debugging Computer Vision Models Guillaume Leclerc Hadi Salman Andrew Ilyas Sai H. Vemprala Logan Engstrom ... Pengchuan Zhang Shibani Santurkar Greg Yang Ashish Kapoor Aleksander Madry 120 42 0 07 Jun 2021
Understanding Neural Code Intelligence Through Program Simplification Md Rafiqul Islam Rabin Vincent J. Hellendoorn Mohammad Amin Alipour AAML 108 61 0 07 Jun 2021
Deep Learning-based Type Identification of Volumetric MRI Sequences Jean Pablo Vieira de Mello T. M. Paixão Rodrigo Berriel M. Reyes C. Badue Alberto F. de Souza Thiago Oliveira-Santos 20 10 0 06 Jun 2021
Causal Abstractions of Neural Networks Atticus Geiger Hanson Lu Thomas Icard Christopher Potts NAI CML 80 246 0 06 Jun 2021
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning Yatao Bian Yu Rong Tingyang Xu Jiaxiang Wu Andreas Krause Junzhou Huang 126 16 0 05 Jun 2021
BERTnesia: Investigating the capture and forgetting of knowledge in BERT Jonas Wallat Jaspreet Singh Avishek Anand CLL KELM 148 60 0 05 Jun 2021
Towards Equal Gender Representation in the Annotations of Toxic Language Detection Elizabeth Excell Noura Al Moubayed 34 14 0 04 Jun 2021
Exploring Distantly-Labeled Rationales in Neural Network Models Quzhe Huang Shengqi Zhu Yansong Feng Dongyan Zhao 59 10 0 03 Jun 2021
Lymph Node Graph Neural Networks for Cancer Metastasis Prediction M. Kazmierski B. Haibe-Kains 71 6 0 03 Jun 2021
Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution Jiacheng Xu Greg Durrett 99 16 0 03 Jun 2021
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations Peter Hase Harry Xie Joey Tianyi Zhou OODD LRM FAtt 125 91 0 01 Jun 2021
Using Integrated Gradients and Constituency Parse Trees to explain Linguistic Acceptability learnt by BERT Anmol Nayak Hariprasad Timmapathini 56 5 0 01 Jun 2021
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods E. Amparore Alan Perotti P. Bajardi FAtt 86 68 0 01 Jun 2021
Distribution Matching for Rationalization Yongfeng Huang Yujun Chen Yulun Du Zhilin Yang OOD 67 18 0 01 Jun 2021
Memory Wrap: a Data-Efficient and Interpretable Extension to Image Classification Models B. La Rosa Roberto Capobianco Daniele Nardi VLM 50 9 0 01 Jun 2021
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals Asma Ghandeharioun Been Kim Chun-Liang Li Brendan Jou B. Eoff Rosalind W. Picard AAML 102 54 0 31 May 2021
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores Giang Nguyen Daeyoung Kim Anh Totti Nguyen FAtt 139 89 0 31 May 2021
Bounded logit attention: Learning to explain image classifiers Thomas Baumhauer D. Slijepcevic Matthias Zeppelzauer FAtt 64 2 0 31 May 2021
How Low is Too Low? A Computational Perspective on Extremely Low-Resource Languages Rachit Bansal Himanshu Choudhary Ravneet Punia Niko Schenk Jacob L Dahl Émilie Pagé-Perron OffRL 21 13 0 30 May 2021
EDDA: Explanation-driven Data Augmentation to Improve Explanation Faithfulness Ruiwen Li Zhibo Zhang Jiani Li C. Trabelsi Scott Sanner Jongseong Jang Yeonjeong Jeong Dongsub Shim AAML 47 1 0 29 May 2021
A General Taylor Framework for Unifying and Revisiting Attribution Methods Huiqi Deng Na Zou Mengnan Du Weifu Chen Guo-Can Feng Helen Zhou TDI FAtt 81 2 0 28 May 2021
Do not explain without context: addressing the blind spot of model explanations Katarzyna Wo'znica Katarzyna Pkekala Hubert Baniecki Wojciech Kretowicz El.zbieta Sienkiewicz P. Biecek 61 1 0 28 May 2021
ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation Vijit Malik Rishabh Sanjay S. Nigam Kripabandhu Ghosh S. Guha Arnab Bhattacharya Ashutosh Modi ELM AILaw 117 149 0 28 May 2021
Towards Interpretable Attention Networks for Cervical Cancer Analysis Ruiqiao Wang M. Armin Simon Denman L. Petersson David Ahmedt-Aristizabal 35 4 0 27 May 2021
CrystalCandle: A User-Facing Model Explainer for Narrative Explanations Jilei Yang Diana M. Negoescu P. Ahammad 37 1 0 27 May 2021
Fooling Partial Dependence via Data Poisoning Hubert Baniecki Wojciech Kretowicz P. Biecek AAML 83 23 0 26 May 2021
XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data Eloise Withnell Xiaoyu Zhang Kai Sun Yike Guo 78 67 0 26 May 2021
Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding Zizhao Zhang Han Zhang Long Zhao Ting Chen Sercan O. Arik Tomas Pfister ViT 102 174 0 26 May 2021
Can We Faithfully Represent Masked States to Compute Shapley Values on a DNN? Jie Ren Zhanpeng Zhou Qirui Chen Quanshi Zhang FAtt TDI 84 8 0 22 May 2021
Explainable Machine Learning with Prior Knowledge: An Overview Katharina Beckh Sebastian Müller Matthias Jakobs Vanessa Toborek Hanxiao Tan Raphael Fischer Pascal Welke Sebastian Houben Laura von Rueden XAI 82 28 0 21 May 2021
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks Thorben Funke Megha Khosla Mandeep Rathee Avishek Anand FAtt 105 41 0 18 May 2021
Self-interpretable Convolutional Neural Networks for Text Classification Wei Zhao Rahul Singh Tarun Joshi Agus Sudjianto V. Nair FAtt MILM 54 6 0 18 May 2021
Fine-grained Interpretation and Causation Analysis in Deep NLP Models Hassan Sajjad Narine Kokhlikyan Fahim Dalvi Nadir Durrani MILM 83 8 0 17 May 2021
A Review on Explainability in Multimodal Deep Neural Nets Gargi Joshi Rahee Walambe K. Kotecha 138 142 0 17 May 2021
How to Explain Neural Networks: an Approximation Perspective Hangcheng Dong Bingguo Liu Fengdong Chen Dong Ye Guodong Liu FAtt 55 1 0 17 May 2021
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts Gesina Schwalbe Bettina Finzel XAI 155 198 0 15 May 2021
Cause and Effect: Hierarchical Concept-based Explanation of Neural Networks Mohammad Nokhbeh Zaeem Majid Komeili CML 74 9 0 14 May 2021
Information-theoretic Evolution of Model Agnostic Global Explanations Sukriti Verma Nikaash Puri Piyush B. Gupta Balaji Krishnamurthy FAtt 64 0 0 14 May 2021
Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations Matthew Watson Bashar Awwad Shiekh Hasan Noura Al Moubayed OOD 54 23 0 14 May 2021
Biometrics: Trust, but Verify Anil K. Jain Debayan Deb Joshua J. Engelsma FaML 91 84 0 14 May 2021
Bias, Fairness, and Accountability with AI and ML Algorithms Neng-Zhi Zhou Zach Zhang V. Nair Harsh Singhal Jie Chen Agus Sudjianto FaML 125 9 0 13 May 2021
Sanity Simulations for Saliency Methods Joon Sik Kim Gregory Plumb Ameet Talwalkar FAtt 104 18 0 13 May 2021
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization Damien Teney Ehsan Abbasnejad Simon Lucey Anton Van Den Hengel 119 90 0 12 May 2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks Eric Wong Shibani Santurkar Aleksander Madry FAtt 67 92 0 11 May 2021
Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity Ryan Henderson Djork-Arné Clevert F. Montanari 91 27 0 11 May 2021
Rationalization through Concepts Diego Antognini Boi Faltings FAtt 124 22 0 11 May 2021