[19] Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. Advances in neural information processing systems, 30, 2017.
[20] Zachary C. Lipton and Jacob Steinhardt. Troubling Trends in Machine Learning Scholarship, July 2018.
[21] Judea Pearl and Dana Mackenzie. The Book of Why: The New Science of Cause and Effect. Basic books, 2018.
[22] Judea Pearl. Causality. Cambridge university press, 2009.
[23] Antonio Torralba. Modeling global scene factors in attention. JOSA A, 20(7):1407-1418, 2003.
[24] Neil DB Bruce. Features that draw visual attention: An information theoretic perspective. Neurocomputing, 65:125-133, 2005.
[25] Lai Jiang, Zhe Wang, Mai Xu, and Zulin Wang. Image saliency prediction in transformed domain: A deep complex neural network method. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 8521-8528, 2019.
[26] WM Boothby. An introduction to differential geometry and riemannian manifolds, 1975.
[27] Anne Keitel, Moritz M Daum, et al. The use of intonation for turn anticipation in observed conversations without visual signals as source of information. Frontiers in psychology, 6(108):265-273, 2015.
[28] Stephen C. Levinson and Francisco Torreira. Timing in turn-taking and its implications for processing models of language. Frontiers in Psychology, 6, June 2015. ISSN 1664-1078. doi: 10.3389/fpsyg.2015.00731.
[29] Dan Bohus and Eric Horvitz. Managing Human-Robot Engagement with Forecasts and... um... Hesitations. Proceedings of the 16th International Conference on Multimodal Interaction, page 8, 2014.
[30] Felix van Doorn. Rituals of Leaving: Predictive Modelling of Leaving Behaviour in Conversation. Master of Science Thesis, Delft University of Technology, 2018.
[31] Sanjay Bilakhia, Stavros Petridis, and Maja Pantic. Audiovisual Detection of Behavioural Mimicry. In 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, pages 123-128, Geneva, Switzerland, September 2013. IEEE. ISBN 978-0-7695-5048-0. doi: 10.1109/ACII.2013.27.
[32] David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, and Jan Gasthaus. High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes. In H. Wallach, H. Larochelle, A. Beygelzimer, F. dAlché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019.
[33] David Salinas, Valentin Flunkert, and Jan Gasthaus. DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. arXiv:1704.04110 [cs, stat], February 2019.
[34] Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski. Deep state space models for time series forecasting. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018.
[35] Thomas M Cover and Joy A Thomas. Elements of Information Theory. Wiley, 2006.
[36] A Verdugo Lazo and P Rathie. On the entropy of continuous probability distributions (corresp.). IEEE Transactions on Information Theory, 24(1):120-122, 1978.
[37] Tim Salzmann, Boris Ivanovic, Punarjay Chakravarty, and Marco Pavone. Trajectory++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data. arXiv:2001.03093 [cs], January 2021.
[38] David Ha and Douglas Eck. A Neural Representation of Sketch Drawings. arXiv:1704.03477 [cs, stat], May 2017.
[39] Ilia Kulikov, Sean Welleck, and Kyunghyun Cho. Mode recovery in neural autoregressive sequence modeling. arXiv preprint arXiv:2106.05459, 2021.
[40] Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, and Martin Vechev. Adversarial attacks on probabilistic autoregressive forecasting models. In International Conference on Machine Learning, pages 2356-2365. PMLR, 2020.