Learn to explain yourself, when you can: Equipping Concept Bottleneck
Models with the ability to abstain on their concept predictions

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Learn to explain yourself, when you can: Equipping Concept Bottleneck Models with the ability to abstain on their concept predictions

21 November 2022

Daniele Magazzeni

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Papers citing "Learn to explain yourself, when you can: Equipping Concept Bottleneck Models with the ability to abstain on their concept predictions"

12 / 12 papers shown

Title
Towards learning to explain with concept bottleneck models: mitigating information leakage J. Lockhart Nicolas Marchesotti Daniele Magazzeni Manuela Veloso 44 7 0 07 Nov 2022
Promises and Pitfalls of Black-Box Concept Learning Models Anita Mahinpei Justin Clark Isaac Lage Finale Doshi-Velez Weiwei Pan 83 96 0 24 Jun 2021
Do Concept Bottleneck Models Learn as Intended? Andrei Margeloiu Matthew Ashman Umang Bhatt Yanzhi Chen M. Jamnik Adrian Weller SLR 50 97 0 10 May 2021
Learning Transferable Visual Models From Natural Language Supervision Alec Radford Jong Wook Kim Chris Hallacy Aditya A. Ramesh Gabriel Goh ... Amanda Askell Pamela Mishkin Jack Clark Gretchen Krueger Ilya Sutskever CLIP VLM 967 29,731 0 26 Feb 2021
Concept Bottleneck Models Pang Wei Koh Thao Nguyen Y. S. Tang Stephen Mussmann Emma Pierson Been Kim Percy Liang 99 833 0 09 Jul 2020
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 127 6,293 0 22 Oct 2019
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 283 8,920 0 25 Aug 2017
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 728 132,199 0 12 Jun 2017
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,322 0 10 Dec 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 836 9,345 0 06 Jun 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.0K 150,260 0 22 Dec 2014
Deep Learning in Neural Networks: An Overview Jürgen Schmidhuber HAI 246 16,373 0 30 Apr 2014

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