[60] Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, and Fabien Moutarde. THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling. arXiv:2110.06607 [cs], January 2022.
[61] Sandra Carrasco, David Fernández Llorca, and Miguel Ángel Sotelo. SCOUT: Socially-Consistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs. arXiv:2102.06361 [cs], May 2021.
[62] Andrey Rudenko, Luigi Palmieri, Michael Herman, Kris M Kitani, Dariu M Gavrila, and Kai O Arras. Human motion trajectory prediction: A survey. The International Journal of Robotics Research, 39(8): 895–935, 2020.
[63] Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuScenes: A Multimodal Dataset for Autonomous Driving. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11618-11628, Seattle, WA, USA, June 2020. IEEE. ISBN 978-1-72817-168-5. doi: 10.1109/CVPR42600.2020.01164.
[64] David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller. How to explain individual classification decisions. The Journal of Machine Learning Research, 11: 1803-1831, 2010.
[65] Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg. Smoothgrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825, 2017.
[66] Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. Learning important features through propagating activation differences. In International conference on machine learning, pages 3145-3153. PMLR, 2017.
[67] Sarah Adel Bargal, Andrea Zunino, Donghyun Kim, Jianming Zhang, Vittorio Murino, and Stan Sclaroff. Excitation Backprop for RNNs. arXiv:1711.06778 [cs], March 2018.
[68] Matthew D Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. In European Conference on Computer Vision, pages 818-833. Springer, 2014.
[69] Felix Mujkanovic, Vanja Doskoč, Martin Schirneck, Patrick Schäfer, and Tobias Friedrich. timeXplain - A Framework for Explaining the Predictions of Time Series Classifiers. arXiv:2007.07606 [cs, stat], July 2020.
[70] Harini Suresh, Nathan Hunt, Alistair Johnson, Leo Anthony Celi, Peter Szolovits, and Marzyeh Ghassemi. Clinical intervention prediction and understanding using deep networks. arXiv preprint arXiv:1705.08498, 2017.
[71] Aya Abdelsalam Ismail, Mohamed Gunady, Héctor Corrada Bravo, and Soheil Feizi. Benchmarking Deep Learning Interpretability in Time Series Predictions. arXiv:2010.13924 [cs, stat], October 2020.
[72] Edward Choi, Mohammad Taha Bahadori, Joshua A. Kulas, Andy Schuetz, Walter F. Stewart, and Jimeng Sun. RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. arXiv:1608.05745 [cs], August 2016.
[73] Ahmed M Alaa and Mihaela van der Schaar. Attentive state-space modeling of disease progression. Advances in neural information processing systems, 32, 2019.
[74] Madhurima Panja, Uttam Kumar, and Tanujit Chakraborty. An Interpretable Probabilistic Autoregressive Neural Network Model for Time Series Forecasting. arXiv:2204.09640 [cs, stat], April 2022.
[75] Longyuan Li, Junchi Yan, Xiaokang Yang, and Yaohui Jin. Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting. arXiv:2102.00397 [cs, stat], January 2021.
[76] Shalmali Joshi, Sonali Parbhoo, and Finale Doshi-Velez. Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty, September 2021.