Explaining Recurrent Neural Network Predictions in Sentiment Analysis

22 June 2017

Papers citing "Explaining Recurrent Neural Network Predictions in Sentiment Analysis"

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Title
SPES: Spectrogram Perturbation for Explainable Speech-to-Text Generation Dennis Fucci Marco Gaido Beatrice Savoldi Matteo Negri Mauro Cettolo L. Bentivogli 57 1 0 03 Nov 2024
Explaining Text Similarity in Transformer Models Alexandros Vasileiou Oliver Eberle 43 7 0 10 May 2024
Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation Paulo Yanez Sarmiento Simon Witzke Nadja Klein Bernhard Y. Renard FAtt AAML 40 0 0 22 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
HealthPrism: A Visual Analytics System for Exploring Children's Physical and Mental Health Profiles with Multimodal Data Zhihan Jiang Handi Chen Rui Zhou Jing Deng Xinchen Zhang Running Zhao Cong Xie Yifang Wang Edith C.H. Ngai 19 4 0 23 Jul 2023
DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications Adam Ivankay Mattia Rigotti P. Frossard OOD MedIm 29 1 0 05 Jul 2023
Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation N. Jethani A. Saporta Rajesh Ranganath FAtt 29 11 0 24 Feb 2023
Explaining text classifiers through progressive neighborhood approximation with realistic samples Yi Cai Arthur Zimek Eirini Ntoutsi Gerhard Wunder AI4TS 22 0 0 11 Feb 2023
A Biomedical Knowledge Graph for Biomarker Discovery in Cancer Md. Rezaul Karim Lina Molinas Comet Oya Beyan Dietrich-Rebholz Schuhmann Stefan Decker 22 2 0 09 Feb 2023
Explainability and Robustness of Deep Visual Classification Models Jindong Gu AAML 41 2 0 03 Jan 2023
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces Pattarawat Chormai J. Herrmann Klaus-Robert Muller G. Montavon FAtt 48 17 0 30 Dec 2022
Explainable AI for Bioinformatics: Methods, Tools, and Applications Md. Rezaul Karim Tanhim Islam Oya Beyan Christoph Lange Michael Cochez Dietrich-Rebholz Schuhmann Stefan Decker 29 68 0 25 Dec 2022
Explaining Image Classifiers with Multiscale Directional Image Representation Stefan Kolek Robert Windesheim Héctor Andrade-Loarca Gitta Kutyniok Ron Levie 29 4 0 22 Nov 2022
Explainable Artificial Intelligence: Precepts, Methods, and Opportunities for Research in Construction Peter E. D. Love Weili Fang J. Matthews Stuart Porter Hanbin Luo L. Ding XAI 29 7 0 12 Nov 2022
Feature Importance for Time Series Data: Improving KernelSHAP M. Villani J. Lockhart Daniele Magazzeni FAtt AI4TS 40 6 0 05 Oct 2022
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities Subash Neupane Jesse Ables William Anderson Sudip Mittal Shahram Rahimi I. Banicescu Maria Seale AAML 56 71 0 13 Jul 2022
From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation Reduan Achtibat Maximilian Dreyer Ilona Eisenbraun S. Bosse Thomas Wiegand Wojciech Samek Sebastian Lapuschkin FAtt 30 132 0 07 Jun 2022
How explainable are adversarially-robust CNNs? Mehdi Nourelahi Lars Kotthoff Peijie Chen Anh Totti Nguyen AAML FAtt 22 8 0 25 May 2022
Learning to Scaffold: Optimizing Model Explanations for Teaching Patrick Fernandes Marcos Vinícius Treviso Danish Pruthi André F. T. Martins Graham Neubig FAtt 25 22 0 22 Apr 2022
Forward Composition Propagation for Explainable Neural Reasoning Isel Grau Gonzalo Nápoles M. Bello Yamisleydi Salgueiro A. Jastrzębska 22 0 0 23 Dec 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 114 58 0 07 Nov 2021
Double Trouble: How to not explain a text classifier's decisions using counterfactuals synthesized by masked language models? Thang M. Pham Trung H. Bui Long Mai Anh Totti Nguyen 21 7 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 17 44 0 20 Oct 2021
A Framework for Rationale Extraction for Deep QA models Sahana Ramnath Preksha Nema Deep Sahni Mitesh M. Khapra AAML FAtt 17 0 0 09 Oct 2021
Improving Scheduled Sampling with Elastic Weight Consolidation for Neural Machine Translation Michalis Korakakis Andreas Vlachos CLL 31 2 0 13 Sep 2021
Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience G. Chrysostomou Nikolaos Aletras 32 16 0 31 Aug 2021
Levels of explainable artificial intelligence for human-aligned conversational explanations Richard Dazeley Peter Vamplew Cameron Foale Charlotte Young Sunil Aryal F. Cruz 30 90 0 07 Jul 2021
On Guaranteed Optimal Robust Explanations for NLP Models Emanuele La Malfa A. Zbrzezny Rhiannon Michelmore Nicola Paoletti Marta Z. Kwiatkowska FAtt 13 47 0 08 May 2021
Improving the Faithfulness of Attention-based Explanations with Task-specific Information for Text Classification G. Chrysostomou Nikolaos Aletras 21 37 0 06 May 2021
Flexible Instance-Specific Rationalization of NLP Models G. Chrysostomou Nikolaos Aletras 31 14 0 16 Apr 2021
Explainability: Relevance based Dynamic Deep Learning Algorithm for Fault Detection and Diagnosis in Chemical Processes P. Agarwal Melih Tamer H. Budman AAML 13 43 0 22 Mar 2021
DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali Language Md. Rezaul Karim Sumon Dey Tanhim Islam Sagor Sarker Mehadi Hasan Menon Kabir Hossain Bharathi Raja Chakravarthi Md. Azam Hossain Stefan Decker 14 77 0 28 Dec 2020
Towards Robust Explanations for Deep Neural Networks Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 24 63 0 18 Dec 2020
It's All in the Name: A Character Based Approach To Infer Religion Rochana Chaturvedi Sugat Chaturvedi 24 23 0 27 Oct 2020
Interpretation of NLP models through input marginalization Siwon Kim Jihun Yi Eunji Kim Sungroh Yoon MILM FAtt 22 58 0 27 Oct 2020
The elephant in the interpretability room: Why use attention as explanation when we have saliency methods? Jasmijn Bastings Katja Filippova XAI LRM 46 173 0 12 Oct 2020
Explaining Deep Neural Networks Oana-Maria Camburu XAI FAtt 33 26 0 04 Oct 2020
Towards Interpretable Deep Learning Models for Knowledge Tracing Yu Lu De-Wu Wang Qinggang Meng Penghe Chen 17 36 0 13 May 2020
How recurrent networks implement contextual processing in sentiment analysis Niru Maheswaranathan David Sussillo 22 22 0 17 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 44 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
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
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks Aya Abdelsalam Ismail Mohamed K. Gunady L. Pessoa H. C. Bravo S. Feizi AI4TS 25 50 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
Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods Oana-Maria Camburu Eleonora Giunchiglia Jakob N. Foerster Thomas Lukasiewicz Phil Blunsom FAtt AAML 26 60 0 04 Oct 2019
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
Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment Jaap Jumelet Willem H. Zuidema Dieuwke Hupkes LRM 33 37 0 19 Sep 2019
Understanding Memory Modules on Learning Simple Algorithms Kexin Wang Yu Zhou Shaonan Wang Jiajun Zhang Chengqing Zong 34 0 0 01 Jul 2019