Fetching the paper…
Reading the bibliography…
The integration of artificial intelligence into business processes has significantly enhanced decision-making capabilities across various industries such as finance, healthcare, and retail.
Can we open the black box of AI?
Davide Castelvecchi · 2016
Earlier work this paper cites.
Interpretable decision sets: A joint framework for description and prediction
Himabindu Lakkaraju, Stephen H Bach, and Jure Leskovec · 2016
Earlier work this paper cites.
”why should i trust you?” explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
Model-agnostic interpretability of machine learning
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
European union regulations on algorithmic decision-making and a “right to explanation”
Bryce Goodman and Seth Flaxman · 2017
Earlier work this paper cites.
Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
Earlier work this paper cites.
What do we need to build explainable ai systems for the medical domain?
Andreas Holzinger, Chris Biemann, Constantinos S Pattichis, and Douglas B Kell · 2017
Earlier work this paper cites.
Peeking inside the black-box: A survey on explainable artificial intelligence (xai)
Amina Adadi and Mohammed Berrada · 2018
Earlier work this paper cites.
Explainable and Interpretable Models in Computer Vision and Machine Learning
Hugo Jair Escalante, Sergio Escalera, Isabelle Guyon, Xavier Baró, Yağmur Güçlütürk, Umut Güçlü, Marcel van Gerven, and Rob van Lier · 2018
Earlier work this paper cites.
Explainable artificial intelligence: A survey
Filip Karlo Došilović, Mario Brčić, and Nikica Hlupić · 2018
Earlier work this paper cites.
Darpa’s explainable artificial intelligence (xai) program
David Gunning and David Aha · 2019
Earlier work this paper cites.
Remote monitoring of vital signs in diverse non-clinical and clinical scenarios using computer vision systems: A review
Fatema-Tuz-Zohra Khanam, Ali Al-Naji, and Javaan Chahl · 2019
Earlier work this paper cites.
Sarthak Jain and Byron C Wallace · 2019
Earlier work this paper cites.
Explaining explanations in AI
Brent Mittelstadt, Chris Russell, and Sandra Wachter · 2019
Earlier work this paper cites.
Quantifying attention flow in transformers
Samira Abnar and Willem Zuidema · 2020
Cited alongside, same era.
Explainable artificial intelligence: A systematic review
Giulia Vilone and Luca Longo · 2020
Cited alongside, same era.
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, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, et al · 2020
Cited alongside, same era.
Fooling lime and shap: Adversarial attacks on post hoc explanation methods
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Robust explainability: A tutorial on gradient-based attribution methods for deep neural networks
Ian E Nielsen, Dimah Dera, Ghulam Rasool, Ravi P Ramachandran, and Nidhal Carla Bouaynaya · 2022
Later among the works it cites.
Explainability of deep vision-based autonomous driving systems: Review and challenges
Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez, and Matthieu Cord · 2022
Later among the works it cites.
A survey on deep multimodal learning for computer vision: Advances, trends, applications, and datasets
Khaled Bayoudh, Raja Knani, Fayçal Hamdaoui, and Abdellatif Mtibaa · 2022
Later among the works it cites.
The disagreement problem in explainable machine learning: A practitioner’s perspective
Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju · 2022
Later among the works it cites.
Explainable artificial intelligence (xai): What we know and what is left to attain trustworthy artificial intelligence
Sajid Ali, Tamer Abuhmed, Shaker El-Sappagh, Khan Muhammad, Jose M Alonso-Moral, Roberto Confalonieri, Riccardo Guidotti, Javier Del Ser, Natalia Díaz-Rodríguez, and Francisco Herrera · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
xViTCOS: Explainable vision transformer based COVID-19 screening using radiography
Arnab Kumar Mondal, Arnab Bhattacharjee, Parag Singla, and AP Prathosh · 2021
Cited alongside, same era.
Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, and Randy Goebel · 2021
Cited alongside, same era.
Reliable post hoc explanations: Modeling uncertainty in explainability
Dylan Slack, Anna Hilgard, Sameer Singh, and Himabindu Lakkaraju · 2021
Cited alongside, same era.
Perturbation-based methods for explaining deep neural networks: A survey
Maksims Ivanovs, Roberts Kadikis, and Kaspars Ozols · 2021
Cited alongside, same era.
Transformer interpretability beyond attention visualization
Hila Chefer, Shir Gur, and Lior Wolf · 2021
Cited alongside, same era.
Ai explainability 360 toolkit
Vijay Arya, Rachel KE Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C Hoffman, Stephanie Houde, Q Vera Liao, Ronny Luss, Aleksandra Mojsilović, et al · 2021
Cited alongside, same era.
Order in the court: Explainable ai methods prone to disagreement
Michael Neely, Stefan F Schouten, Maurits JR Bleeker, and Ana Lucic · 2021
Cited alongside, same era.
Later among the works it cites.
Explaining through transformer input sampling
Alexandre Englebert, Sédrick Stassin, Géraldin Nanfack, Sidi Ahmed Mahmoudi, Xavier Siebert, Olivier Cornu, and Christophe De Vleeschouwer · 2023
Later among the works it cites.
Interpretability-aware vision transformer
Yao Qiang, Chengyin Li, Prashant Khanduri, and Dongxiao Zhu · 2023
Later among the works it cites.
Defect detection methods for industrial products using deep learning techniques: A review
Alireza Saberironaghi, Jing Ren, and Moustafa El-Gindy · 2023
Later among the works it cites.
Explainable AI is dead, long live explainable ai! hypothesis-driven decision support using evaluative AI
Tim Miller · 2023
Later among the works it cites.
LeGrad: An explainability method for vision transformers via feature formation sensitivity
Walid Bousselham, Angie Boggust, Sofian Chaybouti, Hendrik Strobelt, and Hilde Kuehne · 2024
Closest in time.
Unlocking the potential of XAI for improved alzheimer’s disease detection and classification using a ViT-GRU model
S M Mahim, Md Ali, Md Hasan, Abdullah Al Nomaan Nafi, Arefin Sadat, Shakib Al Hasan, Bryar Shareef, Md Manjurul Ahsan, Md Islam, Md Sipon Miah, and Ming-Bo Niu · 2024
Closest in time.
Enhancing breast cancer segmentation and classification: An ensemble deep convolutional neural network and U-net approach on ultrasound images
Md. Rakibul Islam, Md Ali, Abdullah Al Nomaan Nafi, Md Alam, Tapan Godder, Md Sipon Miah, and Md Islam · 2024
Closest in time.
On the faithfulness of vision transformer explanations
Junyi Wu, Weitai Kang, Hao Tang, Yuan Hong, and Yan Yan · 2024
Closest in time.
XAI personalized recommendation algorithm using ViT and K-Means
Young-Bok Cho · 2024
Closest in time.