Benchmarking Deep Learning Interpretability in Time Series Predictions

Benchmarking Deep Learning Interpretability in Time Series Predictions

26 October 2020

Aya Abdelsalam Ismail

Mohamed K. Gunady

Papers citing "Benchmarking Deep Learning Interpretability in Time Series Predictions"

19 / 19 papers shown

Title
Feature Importance Depends on Properties of the Data: Towards Choosing the Correct Explanations for Your Data and Decision Trees based Models Célia Wafa Ayad Thomas Bonnier Benjamin Bosch Sonali Parbhoo Jesse Read FAtt XAI 103 0 0 11 Feb 2025
Unifying Prediction and Explanation in Time-Series Transformers via Shapley-based Pretraining Qisen Cheng Jinming Xing Chang Xue Xiaoran Yang AI4TS 33 3 0 28 Jan 2025
Explanation Space: A New Perspective into Time Series Interpretability Shahbaz Rezaei Xin Liu AI4TS 34 1 0 02 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 67 28 0 30 Aug 2024
On the Evaluation Consistency of Attribution-based Explanations Jiarui Duan Haoling Li Haofei Zhang Hao Jiang Mengqi Xue Li Sun Mingli Song Jie Song XAI 46 0 0 28 Jul 2024
CausalConceptTS: Causal Attributions for Time Series Classification using High Fidelity Diffusion Models Juan Miguel Lopez Alcaraz Nils Strodthoff DiffM AI4TS CML 29 2 0 24 May 2024
WEITS: A Wavelet-enhanced residual framework for interpretable time series forecasting Ziyou Guo Yan Sun Tieru Wu AI4TS 38 2 0 17 May 2024
Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition Xiao-lan Wu P. Bell A. Rajan 19 5 0 29 May 2023
Data-Centric Debugging: mitigating model failures via targeted data collection Sahil Singla Atoosa Malemir Chegini Mazda Moayeri Soheil Feiz 16 4 0 17 Nov 2022
Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts Chirag Raman Hayley Hung Marco Loog 16 3 0 01 Jun 2022
Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choices Dixian Zhu Xiaodong Wu Tianbao Yang 28 10 0 27 Mar 2022
Identifying Suitable Tasks for Inductive Transfer Through the Analysis of Feature Attributions Alexander Pugantsov R. McCreadie 18 0 0 02 Feb 2022
HIVE: Evaluating the Human Interpretability of Visual Explanations Sunnie S. Y. Kim Nicole Meister V. V. Ramaswamy Ruth C. Fong Olga Russakovsky 66 114 0 06 Dec 2021
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View Di Jin Elena Sergeeva W. Weng Geeticka Chauhan Peter Szolovits OOD 31 55 0 05 Dec 2021
Improving Deep Learning Interpretability by Saliency Guided Training Aya Abdelsalam Ismail H. C. Bravo S. Feizi FAtt 20 79 0 29 Nov 2021
Temporal Dependencies in Feature Importance for Time Series Predictions Kin Kwan Leung Clayton Rooke Jonathan Smith S. Zuberi M. Volkovs OOD AI4TS 23 24 0 29 Jul 2021
Do Feature Attribution Methods Correctly Attribute Features? Yilun Zhou Serena Booth Marco Tulio Ribeiro J. Shah FAtt XAI 22 132 0 27 Apr 2021
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset Chuizheng Meng Loc Trinh Nan Xu Yan Liu 24 30 0 12 Feb 2021
What went wrong and when? Instance-wise Feature Importance for Time-series Models S. Tonekaboni Shalmali Joshi Kieran Campbell D. Duvenaud Anna Goldenberg FAtt OOD AI4TS 49 14 0 05 Mar 2020