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To Softmax, or not to Softmax: that is the question when applying Active
  Learning for Transformer Models

To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer Models

6 October 2022
Julius Gonsior
C. Falkenberg
Silvio Magino
Anja Reusch
Maik Thiele
Wolfgang Lehner
    UQCV
ArXivPDFHTML

Papers citing "To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer Models"

3 / 3 papers shown
Title
Small-Text: Active Learning for Text Classification in Python
Small-Text: Active Learning for Text Classification in Python
Christopher Schröder
Lydia Muller
A. Niekler
Martin Potthast
CLIP
VLM
AI4CE
25
23
0
21 Jul 2021
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
268
5,635
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
247
9,042
0
06 Jun 2015
1