Making deep neural networks right for the right scientific reasons by interacting with their explanations

15 January 2020

Papers citing "Making deep neural networks right for the right scientific reasons by interacting with their explanations"

21 / 121 papers shown

Title
When and How to Fool Explainable Models (and Humans) with Adversarial Examples Jon Vadillo Roberto Santana Jose A. Lozano SILM AAML 36 11 0 05 Jul 2021
Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations Bodhisattwa Prasad Majumder Oana-Maria Camburu Thomas Lukasiewicz Julian McAuley 25 35 0 25 Jun 2021
Interactive Label Cleaning with Example-based Explanations Stefano Teso A. Bontempelli Fausto Giunchiglia Andrea Passerini 30 45 0 07 Jun 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 22 28 0 21 May 2021
A Review on Explainability in Multimodal Deep Neural Nets Gargi Joshi Rahee Walambe K. Kotecha 26 138 0 17 May 2021
Explanation-Based Human Debugging of NLP Models: A Survey Piyawat Lertvittayakumjorn Francesca Toni LRM 37 79 0 30 Apr 2021
Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study Luyang Luo Hao Chen Yongjie Xiao Yanning Zhou Xi Wang ... Zaiyi Liu X. B. Fang E. Tsougenis Huangjing Lin Pheng-Ann Heng 37 6 0 21 Apr 2021
Efficient Explanations from Empirical Explainers Robert Schwarzenberg Nils Feldhus Sebastian Möller FAtt 29 9 0 29 Mar 2021
Human-in-the-loop Handling of Knowledge Drift A. Bontempelli Fausto Giunchiglia Andrea Passerini Stefano Teso 14 6 0 27 Mar 2021
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography A. Barnett F. Schwartz Chaofan Tao Chaofan Chen Yinhao Ren J. Lo Cynthia Rudin 31 133 0 23 Mar 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 59 653 0 20 Mar 2021
Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Do P. Schramowski Cigdem Turan Nico Andersen Constantin Rothkopf Kristian Kersting 25 281 0 08 Mar 2021
EnD: Entangling and Disentangling deep representations for bias correction Enzo Tartaglione C. Barbano Marco Grangetto 26 124 0 02 Mar 2021
A design of human-like robust AI machines in object identification Bao-Gang Hu Weiming Dong 27 2 0 07 Jan 2021
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 21 18 0 10 Dec 2020
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations Wolfgang Stammer P. Schramowski Kristian Kersting FAtt 14 107 0 25 Nov 2020
DeepVir -- Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19 Aanchal Mongia Stuti Jain Émilie Chouzenoux A. Majumdar 8 0 0 22 Sep 2020
Alfie: An Interactive Robot with a Moral Compass Cigdem Turan P. Schramowski Constantin Rothkopf Kristian Kersting LM&Ro 11 0 0 11 Sep 2020
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation L. Guan Mudit Verma Sihang Guo Ruohan Zhang Subbarao Kambhampati 43 42 0 26 Jun 2020
LIMEADE: From AI Explanations to Advice Taking Benjamin Charles Germain Lee Doug Downey Kyle Lo Daniel S. Weld 108 5 0 09 Mar 2020
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems Laura von Rueden S. Mayer Katharina Beckh B. Georgiev Sven Giesselbach ... Rajkumar Ramamurthy Michal Walczak Jochen Garcke Christian Bauckhage Jannis Schuecker 34 626 0 29 Mar 2019