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Improving Explainability of Softmax Classifiers Using a Prototype-Based
  Joint Embedding Method

Improving Explainability of Softmax Classifiers Using a Prototype-Based Joint Embedding Method

2 July 2024
Hilarie Sit
Brendan Keith
Karianne Bergen
ArXivPDFHTML

Papers citing "Improving Explainability of Softmax Classifiers Using a Prototype-Based Joint Embedding Method"

3 / 3 papers shown
Title
Learning with Mixture of Prototypes for Out-of-Distribution Detection
Learning with Mixture of Prototypes for Out-of-Distribution Detection
Haodong Lu
Dong Gong
Shuo Wang
Jason Xue
Lina Yao
Kristen Moore
OODD
58
22
0
05 Feb 2024
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,675
0
05 Dec 2016
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
UQCV
BDL
285
9,145
0
06 Jun 2015
1