"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach

23 December 2016

Papers citing ""What is Relevant in a Text Document?": An Interpretable Machine Learning Approach"

46 / 46 papers shown

Title
Path Analysis for Effective Fault Localization in Deep Neural Networks Soroush Hashemifar Saeed Parsa A. Kalaee AAML 49 0 0 28 Jan 2025
Comparison of decision trees with Local Interpretable Model-Agnostic Explanations (LIME) technique and multi-linear regression for explaining support vector regression model in terms of root mean square error (RMSE) values Amit Thombre FAtt 13 1 0 10 Apr 2024
Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification Md. Shajalal Sebastian Denef Md. Rezaul Karim Alexander Boden Gunnar Stevens XAI 24 5 0 31 Oct 2023
A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI) Timo Speith Markus Langer 31 12 0 26 Jul 2023
Neighboring Words Affect Human Interpretation of Saliency Explanations Tim Dockhorn Yaoliang Yu Heike Adel Mahdi Zolnouri V. Nia FAtt MILM 38 3 0 04 May 2023
Multi-resolution Interpretation and Diagnostics Tool for Natural Language Classifiers P. Jalali Nengfeng Zhou Yufei Yu AAML 33 0 0 06 Mar 2023
Feature construction using explanations of individual predictions Boštjan Vouk Matej Guid Marko Robnik-Šikonja FAtt 27 10 0 23 Jan 2023
Optimizing Explanations by Network Canonization and Hyperparameter Search Frederik Pahde Galip Umit Yolcu Alexander Binder Wojciech Samek Sebastian Lapuschkin 60 11 0 30 Nov 2022
An Interpretability Evaluation Benchmark for Pre-trained Language Models Ya-Ming Shen Lijie Wang Ying-Cong Chen Xinyan Xiao Jing Liu Hua Wu 39 4 0 28 Jul 2022
A Fine-grained Interpretability Evaluation Benchmark for Neural NLP Lijie Wang Yaozong Shen Shu-ping Peng Shuai Zhang Xinyan Xiao Hao Liu Hongxuan Tang Ying-Cong Chen Hua Wu Haifeng Wang ELM 19 21 0 23 May 2022
Human Interpretation of Saliency-based Explanation Over Text Hendrik Schuff Alon Jacovi Heike Adel Yoav Goldberg Ngoc Thang Vu MILM XAI FAtt 148 39 0 27 Jan 2022
Diagnosing AI Explanation Methods with Folk Concepts of Behavior Alon Jacovi Jasmijn Bastings Sebastian Gehrmann Yoav Goldberg Katja Filippova 36 15 0 27 Jan 2022
Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities Waddah Saeed C. Omlin XAI 38 414 0 11 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 120 60 0 07 Nov 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 19 45 0 20 Oct 2021
Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing Qian Liu Dejian Yang Jiahui Zhang Jiaqi Guo Bin Zhou Jian-Guang Lou 51 41 0 22 Sep 2021
A Comprehensive Review on Summarizing Financial News Using Deep Learning Saurabh Kamal Sahil Sharma AIFin 18 2 0 21 Sep 2021
Explaining Bayesian Neural Networks Kirill Bykov Marina M.-C. Höhne Adelaida Creosteanu Klaus-Robert Muller Frederick Klauschen Shinichi Nakajima Marius Kloft BDL AAML 36 25 0 23 Aug 2021
Robustness Tests of NLP Machine Learning Models: Search and Semantically Replace Rahul Singh Karan Jindal Yufei Yu Hanyu Yang Tarun Joshi Matthew A. Campbell Wayne B. Shoumaker 58 2 0 20 Apr 2021
Local Interpretations for Explainable Natural Language Processing: A Survey Siwen Luo Hamish Ivison S. Han Josiah Poon MILM 43 48 0 20 Mar 2021
Towards Robust Explanations for Deep Neural Networks Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 35 63 0 18 Dec 2020
Why model why? Assessing the strengths and limitations of LIME Jurgen Dieber S. Kirrane FAtt 26 97 0 30 Nov 2020
It's All in the Name: A Character Based Approach To Infer Religion Rochana Chaturvedi Sugat Chaturvedi 27 23 0 27 Oct 2020
Interpreting convolutional networks trained on textual data Reza Marzban Christopher Crick FAtt 27 3 0 20 Oct 2020
SHAP values for Explaining CNN-based Text Classification Models Wei Zhao Tarun Joshi V. Nair Agus Sudjianto FAtt 28 36 0 26 Aug 2020
Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification Cristina Garbacea Mengtian Guo Samuel Carton Qiaozhu Mei 19 28 0 31 Jul 2020
Towards Interpretable Deep Learning Models for Knowledge Tracing Yu Lu De-Wu Wang Qinggang Meng Penghe Chen 25 36 0 13 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 43 371 0 30 Apr 2020
Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness? Alon Jacovi Yoav Goldberg XAI 43 567 0 07 Apr 2020
Ontology-based Interpretable Machine Learning for Textual Data Phung Lai Nhathai Phan Han Hu Anuja Badeti David Newman Dejing Dou 9 8 0 01 Apr 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 51 82 0 17 Mar 2020
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI L. Arras Ahmed Osman Wojciech Samek XAI AAML 21 150 0 16 Mar 2020
Interpretability of machine learning based prediction models in healthcare Gregor Stiglic Primož Kocbek Nino Fijačko Marinka Zitnik K. Verbert Leona Cilar AI4CE 35 373 0 20 Feb 2020
Towards Explainable Artificial Intelligence Wojciech Samek K. Müller XAI 32 436 0 26 Sep 2019
Explaining and Interpreting LSTMs L. Arras Jose A. Arjona-Medina Michael Widrich G. Montavon Michael Gillhofer K. Müller Sepp Hochreiter Wojciech Samek FAtt AI4TS 21 79 0 25 Sep 2019
Layerwise Relevance Visualization in Convolutional Text Graph Classifiers Robert Schwarzenberg Marc Hübner David Harbecke Christoph Alt Leonhard Hennig FAtt GNN 15 69 0 24 Sep 2019
A study on the Interpretability of Neural Retrieval Models using DeepSHAP Zeon Trevor Fernando Jaspreet Singh Avishek Anand FAtt AAML 21 68 0 15 Jul 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
The autofeat Python Library for Automated Feature Engineering and Selection F. Horn R. Pack M. Rieger 15 93 0 22 Jan 2019
Automating the search for a patent's prior art with a full text similarity search Lea Helmers F. Horn Franziska Biegler Tim Oppermann K. Müller 24 47 0 10 Jan 2019
Explaining the Unique Nature of Individual Gait Patterns with Deep Learning Fabian Horst Sebastian Lapuschkin Wojciech Samek K. Müller W. Schöllhorn AI4CE 31 207 0 13 Aug 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 56 933 0 20 Jun 2018
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models Jacob R. Kauffmann K. Müller G. Montavon DRL 42 96 0 16 May 2018
Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos Hugo Jair Escalante Heysem Kaya A. A. Salah Sergio Escalera Yağmur Güçlütürk ... Furkan Gürpinar Achmadnoer Sukma Wicaksana Cynthia C. S. Liem Marcel van Gerven R. Lier 25 61 0 02 Feb 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Convolutional Neural Networks for Sentence Classification Yoon Kim AILaw VLM 309 13,373 0 25 Aug 2014