ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2211.11690
  4. Cited By
Learn to explain yourself, when you can: Equipping Concept Bottleneck
  Models with the ability to abstain on their concept predictions
v1v2 (latest)

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

21 November 2022
J. Lockhart
Daniele Magazzeni
Manuela Veloso
ArXiv (abs)PDFHTML

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
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
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?
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
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
CLIPVLM
967
29,731
0
26 Feb 2021
Concept Bottleneck Models
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
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
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
Attention Is All You Need
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan Gomez
Lukasz Kaiser
Illia Polosukhin
3DV
730
132,199
0
12 Jun 2017
Deep Residual Learning for Image Recognition
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
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCVBDL
836
9,345
0
06 Jun 2015
Adam: A Method for Stochastic Optimization
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
Deep Learning in Neural Networks: An Overview
Jürgen Schmidhuber
HAI
246
16,377
0
30 Apr 2014
1