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Learning Global Pairwise Interactions with Bayesian Neural Networks

Learning Global Pairwise Interactions with Bayesian Neural Networks

24 January 2019
Tianyu Cui
Pekka Marttinen
Samuel Kaski
    BDL
ArXivPDFHTML

Papers citing "Learning Global Pairwise Interactions with Bayesian Neural Networks"

6 / 6 papers shown
Title
Error-controlled non-additive interaction discovery in machine learning models
Error-controlled non-additive interaction discovery in machine learning models
Winston Chen
Yifan Jiang
William Stafford Noble
Yang Young Lu
50
1
0
17 Feb 2025
Disentangled Explanations of Neural Network Predictions by Finding
  Relevant Subspaces
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
Pattarawat Chormai
J. Herrmann
Klaus-Robert Muller
G. Montavon
FAtt
52
18
0
30 Dec 2022
Toward Explainable AI for Regression Models
Toward Explainable AI for Regression Models
S. Letzgus
Patrick Wagner
Jonas Lederer
Wojciech Samek
Klaus-Robert Muller
G. Montavon
XAI
36
63
0
21 Dec 2021
Explaining Deep Neural Networks and Beyond: A Review of Methods and
  Applications
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Wojciech Samek
G. Montavon
Sebastian Lapuschkin
Christopher J. Anders
K. Müller
XAI
51
82
0
17 Mar 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep
  Networks
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks
Joseph D. Janizek
Pascal Sturmfels
Su-In Lee
FAtt
30
143
0
10 Feb 2020
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
287
9,156
0
06 Jun 2015
1