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. 2110.10038
  4. Cited By
Coalitional Bayesian Autoencoders -- Towards explainable unsupervised
  deep learning

Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learning

19 October 2021
Bang Xiang Yong
Alexandra Brintrup
ArXiv (abs)PDFHTML

Papers citing "Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learning"

28 / 28 papers shown
Title
Bayesian Autoencoders: Analysing and Fixing the Bernoulli likelihood for
  Out-of-Distribution Detection
Bayesian Autoencoders: Analysing and Fixing the Bernoulli likelihood for Out-of-Distribution Detection
Bang Xiang Yong
Tim Pearce
Alexandra Brintrup
OODDUQCV
65
6
0
28 Jul 2021
Multi Agent System for Machine Learning Under Uncertainty in Cyber
  Physical Manufacturing System
Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System
Bang Xiang Yong
Alexandra Brintrup
AI4CE
59
20
0
28 Jul 2021
Bayesian Autoencoders for Drift Detection in Industrial Environments
Bayesian Autoencoders for Drift Detection in Industrial Environments
Bang Xiang Yong
Yasmin Fathy
Alexandra Brintrup
UQCV
61
8
0
28 Jul 2021
Process Outcome Prediction: CNN vs. LSTM (with Attention)
Process Outcome Prediction: CNN vs. LSTM (with Attention)
Hans Weytjens
Jochen De Weerdt
AI4TS
57
44
0
14 Apr 2021
Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series
Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series
Vincent Jacob
Fei Song
Arnaud Stiegler
Bijan Rad
Y. Diao
Nesime Tatbul
AI4TS
62
68
0
10 Oct 2020
A Unifying Review of Deep and Shallow Anomaly Detection
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff
Jacob R. Kauffmann
Robert A. Vandermeulen
G. Montavon
Wojciech Samek
Marius Kloft
Thomas G. Dietterich
Klaus-Robert Muller
UQCV
117
800
0
24 Sep 2020
Deep Learning for Anomaly Detection: A Review
Deep Learning for Anomaly Detection: A Review
Guansong Pang
Chunhua Shen
LongBing Cao
Anton Van Den Hengel
184
925
0
06 Jul 2020
Explainable Deep One-Class Classification
Explainable Deep One-Class Classification
Philipp Liznerski
Lukas Ruff
Robert A. Vandermeulen
Billy Joe Franks
Marius Kloft
Klaus-Robert Muller
57
199
0
03 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
121
6,269
0
22 Oct 2019
Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge
  Computing
Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing
En Li
Liekang Zeng
Zhi Zhou
Xu Chen
52
628
0
04 Oct 2019
Quality of Uncertainty Quantification for Bayesian Neural Network
  Inference
Quality of Uncertainty Quantification for Bayesian Neural Network Inference
Jiayu Yao
Weiwei Pan
S. Ghosh
Finale Doshi-Velez
UQCVBDL
179
113
0
24 Jun 2019
GEE: A Gradient-based Explainable Variational Autoencoder for Network
  Anomaly Detection
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection
Q. Nguyen
Kar Wai Lim
D. Divakaran
K. H. Low
M. Chan
DRL
47
136
0
15 Mar 2019
Explaining Anomalies Detected by Autoencoders Using SHAP
Explaining Anomalies Detected by Autoencoders Using SHAP
Liat Antwarg
Ronnie Mindlin Miller
Bracha Shapira
Lior Rokach
FAttTDI
62
86
0
06 Mar 2019
On the (In)fidelity and Sensitivity for Explanations
On the (In)fidelity and Sensitivity for Explanations
Chih-Kuan Yeh
Cheng-Yu Hsieh
A. Suggala
David I. Inouye
Pradeep Ravikumar
FAtt
75
453
0
27 Jan 2019
Machine Learning and Deep Learning Algorithms for Bearing Fault
  Diagnostics -- A Comprehensive Review
Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics -- A Comprehensive Review
Shen Zhang
Shibo Zhang
Bingnan Wang
T. Habetler
37
256
0
24 Jan 2019
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
Tim Pearce
Felix Leibfried
Alexandra Brintrup
Mohamed H. Zaki
A. Neely
BDLUQCV
62
197
0
12 Oct 2018
Deep learning for time series classification: a review
Deep learning for time series classification: a review
Hassan Ismail Fawaz
Germain Forestier
J. Weber
L. Idoumghar
Pierre-Alain Muller
AI4TSAI4CE
320
2,698
0
12 Sep 2018
A Benchmark for Interpretability Methods in Deep Neural Networks
A Benchmark for Interpretability Methods in Deep Neural Networks
Sara Hooker
D. Erhan
Pieter-Jan Kindermans
Been Kim
FAttUQCV
108
682
0
28 Jun 2018
Instance Selection Improves Geometric Mean Accuracy: A Study on
  Imbalanced Data Classification
Instance Selection Improves Geometric Mean Accuracy: A Study on Imbalanced Data Classification
L. Kuncheva
Álvar Arnaiz-González
J. Díez-Pastor
Iain A. D. Gunn
30
63
0
19 Apr 2018
Interpretation of Neural Networks is Fragile
Interpretation of Neural Networks is Fragile
Amirata Ghorbani
Abubakar Abid
James Zou
FAttAAML
133
867
0
29 Oct 2017
A Unified Approach to Interpreting Model Predictions
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
1.1K
21,939
0
22 May 2017
Learning Important Features Through Propagating Activation Differences
Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar
Peyton Greenside
A. Kundaje
FAtt
201
3,873
0
10 Apr 2017
Axiomatic Attribution for Deep Networks
Axiomatic Attribution for Deep Networks
Mukund Sundararajan
Ankur Taly
Qiqi Yan
OODFAtt
188
5,989
0
04 Mar 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCVBDL
840
5,821
0
05 Dec 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAttFaML
1.2K
16,990
0
16 Feb 2016
Cyclical Learning Rates for Training Neural Networks
Cyclical Learning Rates for Training Neural Networks
L. Smith
ODL
210
2,529
0
03 Jun 2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
  ImageNet Classification
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
VLM
326
18,625
0
06 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.9K
150,115
0
22 Dec 2014
1