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Visualizing the Diversity of Representations Learned by Bayesian Neural Networks
26 January 2022
Dennis Grinwald
Kirill Bykov
Shinichi Nakajima
Marina M.-C. Höhne
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Papers citing
"Visualizing the Diversity of Representations Learned by Bayesian Neural Networks"
7 / 7 papers shown
Title
CoSy: Evaluating Textual Explanations of Neurons
Laura Kopf
P. Bommer
Anna Hedström
Sebastian Lapuschkin
Marina M.-C. Höhne
Kirill Bykov
44
7
0
30 May 2024
Manipulating Feature Visualizations with Gradient Slingshots
Dilyara Bareeva
Marina M.-C. Höhne
Alexander Warnecke
Lukas Pirch
Klaus-Robert Müller
Konrad Rieck
Kirill Bykov
AAML
37
6
0
11 Jan 2024
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science
P. Bommer
M. Kretschmer
Anna Hedström
Dilyara Bareeva
Marina M.-C. Höhne
46
38
0
01 Mar 2023
DORA: Exploring Outlier Representations in Deep Neural Networks
Kirill Bykov
Mayukh Deb
Dennis Grinwald
Klaus-Robert Muller
Marina M.-C. Höhne
21
12
0
09 Jun 2022
NoiseGrad: Enhancing Explanations by Introducing Stochasticity to Model Weights
Kirill Bykov
Anna Hedström
Shinichi Nakajima
Marina M.-C. Höhne
FAtt
17
34
0
18 Jun 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,660
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,136
0
06 Jun 2015
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