Sequential Interpretability: Methods, Applications, and Future Direction for Understanding Deep Learning Models in the Context of Sequential Data

27 April 2020

Papers citing "Sequential Interpretability: Methods, Applications, and Future Direction for Understanding Deep Learning Models in the Context of Sequential Data"

6 / 6 papers shown

Title
Scene Text Recognition Models Explainability Using Local Features M. Ty Rowel Atienza 28 1 0 14 Oct 2023
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 227 201 0 06 Jul 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017
Characterizing Driving Styles with Deep Learning Weishan Dong Jian Li Renjie Yao Changsheng Li Ting Yuan Lanjun Wang 24 104 0 13 Jul 2016
Recurrent Neural Networks for Multivariate Time Series with Missing Values Zhengping Che S. Purushotham Kyunghyun Cho David Sontag Yan Liu AI4TS 207 1,897 0 06 Jun 2016
Effective Approaches to Attention-based Neural Machine Translation Thang Luong Hieu H. Pham Christopher D. Manning 218 7,923 0 17 Aug 2015