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1802.02952
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TSViz: Demystification of Deep Learning Models for Time-Series Analysis
8 February 2018
Shoaib Ahmed Siddiqui
Dominique Mercier
Mohsin Munir
Andreas Dengel
Sheraz Ahmed
FAtt
AI4TS
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Papers citing
"TSViz: Demystification of Deep Learning Models for Time-Series Analysis"
32 / 32 papers shown
Title
Evaluating Simplification Algorithms for Interpretability of Time Series Classification
Felix Marti-Perez
Felix Marti-Perez
Cèsar Ferri
Carlos Monserrat
AI4TS
211
0
0
13 May 2025
Interpreting Outliers in Time Series Data through Decoding Autoencoder
Katharina Prasse
Sascha Marton
Christian Bartelt
Robert Fuder
293
1
0
03 Sep 2024
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction
Melkamu Mersha
Khang Lam
Joseph Wood
Ali AlShami
Jugal Kalita
XAI
AI4TS
660
76
0
30 Aug 2024
CAFO: Feature-Centric Explanation on Time Series Classification
Jaeho Kim
S. Hahn
Yoontae Hwang
Junghye Lee
Seulki Lee
AI4TS
195
2
0
03 Jun 2024
XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification
International Conference on Machine Learning and Applications (ICMLA), 2023
Jacqueline Höllig
Steffen Thoma
Florian Grimm
AI4TS
208
2
0
23 Oct 2023
Visual Explanations with Attributions and Counterfactuals on Time Series Classification
U. Schlegel
Daniela Oelke
Daniel A. Keim
Mennatallah El-Assady
AI4TS
FAtt
251
5
0
14 Jul 2023
Interpretation of Time-Series Deep Models: A Survey
Ziqi Zhao
Yucheng Shi
Shushan Wu
Fan Yang
Wenzhan Song
Ninghao Liu
AI4TS
257
13
0
23 May 2023
Explainable AI for Time Series via Virtual Inspection Layers
Pattern Recognition (Pattern Recogn.), 2023
Johanna Vielhaben
Sebastian Lapuschkin
G. Montavon
Wojciech Samek
XAI
AI4TS
215
40
0
11 Mar 2023
On the Soundness of XAI in Prognostics and Health Management (PHM)
D. Martín
Juan Galán Páez
J. Borrego-Díaz
128
18
0
09 Mar 2023
Privacy Meets Explainability: A Comprehensive Impact Benchmark
S. Saifullah
Dominique Mercier
Adriano Lucieri
Andreas Dengel
Sheraz Ahmed
149
18
0
08 Nov 2022
Explainable AI for clinical and remote health applications: a survey on tabular and time series data
Artificial Intelligence Review (Artif Intell Rev), 2022
Flavio Di Martino
Franca Delmastro
AI4TS
172
126
0
14 Sep 2022
Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerability
International Conference on Information and Knowledge Management (CIKM), 2022
Shahroz Tariq
B. Le
Simon S. Woo
AAML
AI4TS
97
6
0
24 Aug 2022
TSInterpret: A unified framework for time series interpretability
Jacqueline Höllig
Cedric Kulbach
Steffen Thoma
AI4TS
AI4CE
232
5
0
10 Aug 2022
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features
Journal of Artificial Intelligence Research (JAIR), 2022
Taha Belkhouja
J. Doppa
AAML
AI4TS
150
15
0
09 Jul 2022
Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection
Thabang Mathonsi
Terence L van Zyl
144
0
0
25 Feb 2022
TimeREISE: Time-series Randomized Evolving Input Sample Explanation
Italian National Conference on Sensors (INS), 2022
Dominique Mercier
Andreas Dengel
Sheraz Ahmed
AI4TS
88
9
0
16 Feb 2022
Time to Focus: A Comprehensive Benchmark Using Time Series Attribution Methods
International Conference on Agents and Artificial Intelligence (ICAART), 2022
Dominique Mercier
Jwalin Bhatt
Andreas Dengel
Sheraz Ahmed
AI4TS
165
12
0
08 Feb 2022
Time Series Model Attribution Visualizations as Explanations
U. Schlegel
Daniel A. Keim
TDI
BDL
FAtt
AI4TS
XAI
174
16
0
27 Sep 2021
TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models
U. Schlegel
D. Lam
Daniel A. Keim
Daniel Seebacher
FAtt
AI4TS
218
40
0
17 Sep 2021
Explaining Time Series Predictions with Dynamic Masks
International Conference on Machine Learning (ICML), 2021
Jonathan Crabbé
M. Schaar
FAtt
AI4TS
169
104
0
09 Jun 2021
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
Thomas Rojat
Raphael Puget
David Filliat
Javier Del Ser
R. Gelin
Natalia Díaz Rodríguez
XAI
AI4TS
238
160
0
02 Apr 2021
PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification
IEEE International Joint Conference on Neural Network (IJCNN), 2021
Dominique Mercier
Andreas Dengel
Sheraz Ahmed
AI4TS
87
8
0
11 Feb 2021
Explaining Deep Learning Models for Structured Data using Layer-Wise Relevance Propagation
hsan Ullah
André Ríos
Vaibhav Gala
Susan Mckeever
FAtt
158
10
0
26 Nov 2020
Benchmarking adversarial attacks and defenses for time-series data
International Conference on Neural Information Processing (ICONIP), 2020
Shoaib Ahmed Siddiqui
Andreas Dengel
Sheraz Ahmed
AAML
AI4TS
109
15
0
30 Aug 2020
P2ExNet: Patch-based Prototype Explanation Network
International Conference on Neural Information Processing (ICONIP), 2020
Dominique Mercier
Andreas Dengel
Sheraz Ahmed
129
5
0
05 May 2020
Interpreting Deep Models through the Lens of Data
IEEE International Joint Conference on Neural Network (IJCNN), 2020
Dominique Mercier
Shoaib Ahmed Siddiqui
Andreas Dengel
Sheraz Ahmed
TDI
67
3
0
05 May 2020
Time Series Forecasting With Deep Learning: A Survey
Bryan Lim
S. Zohren
AI4TS
AI4CE
256
1,558
0
28 Apr 2020
Sequential Interpretability: Methods, Applications, and Future Direction for Understanding Deep Learning Models in the Context of Sequential Data
B. Shickel
Parisa Rashidi
AI4TS
191
21
0
27 Apr 2020
TSInsight: A local-global attribution framework for interpretability in time-series data
Italian National Conference on Sensors (INS), 2020
Shoaib Ahmed Siddiqui
Dominique Mercier
Andreas Dengel
Sheraz Ahmed
FAtt
AI4TS
162
12
0
06 Apr 2020
What went wrong and when? Instance-wise Feature Importance for Time-series Models
S. Tonekaboni
Shalmali Joshi
Kieran Campbell
David Duvenaud
Anna Goldenberg
FAtt
OOD
AI4TS
208
16
0
05 Mar 2020
TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features
International Conference on Artificial Neural Networks (ICANN), 2019
Mohsin Munir
Shoaib Ahmed Siddiqui
Ferdinand Küsters
Dominique Mercier
Andreas Dengel
Sheraz Ahmed
AI4TS
99
19
0
15 May 2019
Detecting and interpreting myocardial infarction using fully convolutional neural networks
Nils Strodthoff
C. Strodthoff
168
162
0
18 Jun 2018
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