Explaining NonLinear Classification Decisions with Deep Taylor Decomposition

8 December 2015

Papers citing "Explaining NonLinear Classification Decisions with Deep Taylor Decomposition"

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Title
Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images Tom Ron M. Weiler-Sagie Tamir Hazan FAtt MedIm 24 6 0 06 Jun 2022
Optimizing Relevance Maps of Vision Transformers Improves Robustness Hila Chefer Idan Schwartz Lior Wolf ViT 35 37 0 02 Jun 2022
Comparing interpretation methods in mental state decoding analyses with deep learning models A. Thomas Christopher Ré R. Poldrack AI4CE 18 2 0 31 May 2022
Attention Flows for General Transformers Niklas Metzger Christopher Hahn Julian Siber Frederik Schmitt Bernd Finkbeiner 34 0 0 30 May 2022
ViTOL: Vision Transformer for Weakly Supervised Object Localization Saurav Gupta Sourav Lakhotia Abhay Rawat Rahul Tallamraju WSOL 32 21 0 14 Apr 2022
Maximum Entropy Baseline for Integrated Gradients Hanxiao Tan FAtt 18 4 0 12 Apr 2022
CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation S. Gadre Mitchell Wortsman Gabriel Ilharco Ludwig Schmidt Shuran Song CLIP LM&Ro 41 142 0 20 Mar 2022
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience Antonios Mamalakis E. Barnes I. Ebert‐Uphoff 29 73 0 07 Feb 2022
Learning-From-Disagreement: A Model Comparison and Visual Analytics Framework Junpeng Wang Liang Wang Yan Zheng Chin-Chia Michael Yeh Shubham Jain Wei Zhang FAtt 30 12 0 19 Jan 2022
Forward Composition Propagation for Explainable Neural Reasoning Isel Grau Gonzalo Nápoles M. Bello Yamisleydi Salgueiro A. Jastrzębska 22 0 0 23 Dec 2021
Gradient Frequency Modulation for Visually Explaining Video Understanding Models Xinmiao Lin Wentao Bao Matthew Wright Yu Kong FAtt AAML 27 2 0 01 Nov 2021
On Quantitative Evaluations of Counterfactuals Frederik Hvilshoj Alexandros Iosifidis Ira Assent 19 10 0 30 Oct 2021
TSGB: Target-Selective Gradient Backprop for Probing CNN Visual Saliency Lin Cheng Pengfei Fang Yanjie Liang Liao Zhang Chunhua Shen Hanzi Wang FAtt 22 11 0 11 Oct 2021
Focus! Rating XAI Methods and Finding Biases Anna Arias-Duart Ferran Parés Dario Garcia-Gasulla Victor Gimenez-Abalos 26 32 0 28 Sep 2021
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications Dongqi Han Zhiliang Wang Wenqi Chen Ying Zhong Su Wang Han Zhang Jiahai Yang Xingang Shi Xia Yin AAML 24 76 0 23 Sep 2021
Self-learn to Explain Siamese Networks Robustly Chao Chen Yifan Shen Guixiang Ma Xiangnan Kong S. Rangarajan Xi Zhang Sihong Xie 46 5 0 15 Sep 2021
AdjointNet: Constraining machine learning models with physics-based codes S. Karra B. Ahmmed M. Mudunuru AI4CE PINN OOD 16 4 0 08 Sep 2021
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy Christopher J. Anders David Neumann Wojciech Samek K. Müller Sebastian Lapuschkin 29 64 0 24 Jun 2021
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case Natalia Díaz Rodríguez Alberto Lamas Jules Sanchez Gianni Franchi Ivan Donadello Siham Tabik David Filliat P. Cruz Rosana Montes Francisco Herrera 49 77 0 24 Apr 2021
Improving Attribution Methods by Learning Submodular Functions Piyushi Manupriya Tarun Ram Menta S. Jagarlapudi V. Balasubramanian TDI 22 6 0 19 Apr 2021
White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks Meghna P. Ayyar J. Benois-Pineau A. Zemmari FAtt 14 17 0 06 Apr 2021
Explaining Representation by Mutual Information Li Gu SSL FAtt 32 0 0 28 Mar 2021
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 35 25 0 20 Mar 2021
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset Antonios Mamalakis I. Ebert‐Uphoff E. Barnes OOD 28 75 0 18 Mar 2021
Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation G. Agarwal L. Hay I. Iashvili Benjamin Mannix C. McLean Margaret E. Morris S. Rappoccio U. Schubert 43 18 0 26 Nov 2020
Quantifying Explainers of Graph Neural Networks in Computational Pathology Guillaume Jaume Pushpak Pati Behzad Bozorgtabar Antonio Foncubierta-Rodríguez Florinda Feroce A. Anniciello T. Rau Jean-Philippe Thiran M. Gabrani O. Goksel FAtt 26 76 0 25 Nov 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 20 397 0 19 Oct 2020
Quantitative and Qualitative Evaluation of Explainable Deep Learning Methods for Ophthalmic Diagnosis Amitojdeep Singh J. Balaji M. Rasheed Varadharajan Jayakumar R. Raman Vasudevan Lakshminarayanan BDL XAI FAtt 9 29 0 26 Sep 2020
Adaptive Convolution Kernel for Artificial Neural Networks F. B. Tek Ilker Çam D. Karli 14 12 0 14 Sep 2020
Survey of XAI in digital pathology Milda Pocevičiūtė Gabriel Eilertsen Claes Lundström 14 56 0 14 Aug 2020
Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation Kazuya Nishimura Junya Hayashida Chenyang Wang Dai Fei Elmer Ker Ryoma Bise 26 17 0 30 Jul 2020
Sequential Explanations with Mental Model-Based Policies A. Yeung Shalmali Joshi Joseph Jay Williams Frank Rudzicz FAtt LRM 31 15 0 17 Jul 2020
A simple defense against adversarial attacks on heatmap explanations Laura Rieger Lars Kai Hansen FAtt AAML 30 37 0 13 Jul 2020
Higher-Order Explanations of Graph Neural Networks via Relevant Walks Thomas Schnake Oliver Eberle Jonas Lederer Shinichi Nakajima Kristof T. Schütt Klaus-Robert Muller G. Montavon 32 215 0 05 Jun 2020
What went wrong and when? Instance-wise Feature Importance for Time-series Models S. Tonekaboni Shalmali Joshi Kieran Campbell David Duvenaud Anna Goldenberg FAtt OOD AI4TS 51 14 0 05 Mar 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 38 300 0 08 Jan 2020
When Explanations Lie: Why Many Modified BP Attributions Fail Leon Sixt Maximilian Granz Tim Landgraf BDL FAtt XAI 13 132 0 20 Dec 2019
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis D. Slijepcevic Fabian Horst Sebastian Lapuschkin Anna-Maria Raberger Matthias Zeppelzauer Wojciech Samek C. Breiteneder W. Schöllhorn B. Horsak 36 50 0 16 Dec 2019
CXPlain: Causal Explanations for Model Interpretation under Uncertainty Patrick Schwab W. Karlen FAtt CML 34 205 0 27 Oct 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 37 6,111 0 22 Oct 2019
Towards Best Practice in Explaining Neural Network Decisions with LRP M. Kohlbrenner Alexander Bauer Shinichi Nakajima Alexander Binder Wojciech Samek Sebastian Lapuschkin 22 148 0 22 Oct 2019
Software and application patterns for explanation methods Maximilian Alber 33 11 0 09 Apr 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 17 996 0 26 Feb 2019
An Overview of Computational Approaches for Interpretation Analysis Philipp Blandfort Jörn Hees D. Patton 21 2 0 09 Nov 2018
Interpretable Convolutional Neural Networks via Feedforward Design C.-C. Jay Kuo Min Zhang Siyang Li Jiali Duan Yueru Chen 33 155 0 05 Oct 2018
Explaining the Unique Nature of Individual Gait Patterns with Deep Learning Fabian Horst Sebastian Lapuschkin Wojciech Samek K. Müller W. Schöllhorn AI4CE 28 207 0 13 Aug 2018
Contrastive Explanations with Local Foil Trees J. V. D. Waa M. Robeer J. Diggelen Matthieu J. S. Brinkhuis Mark Antonius Neerincx FAtt 19 82 0 19 Jun 2018
Understanding Patch-Based Learning by Explaining Predictions Christopher J. Anders G. Montavon Wojciech Samek K. Müller UQCV FAtt 30 6 0 11 Jun 2018
Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles Alexander Binder M. Bockmayr Miriam Hagele S. Wienert Daniel Heim ... M. Dietel A. Hocke C. Denkert K. Müller Frederick Klauschen AI4CE 8 27 0 28 May 2018
Investigating the influence of noise and distractors on the interpretation of neural networks Pieter-Jan Kindermans Kristof T. Schütt K. Müller Sven Dähne FAtt 19 125 0 22 Nov 2016