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Forecasting human trajectories in complex dynamic environments plays a critical role in autonomous vehicles and intelligent robots.
Social force model for pedestrian dynamics
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Jack M Wang, David J Fleet, and Aaron Hertzmann · 2007
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Causality
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Causal inference in statistics: An overview
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Kota Yamaguchi, Alexander C Berg, Luis E Ortiz, and Tamara L Berg · 2011
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Generative adversarial nets
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Auto-encoding variational bayes
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Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese · 2016
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Deep residual learning for image recognition
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Predicting wide receiver trajectories in american football
Namhoon Lee and Kris M Kitani · 2016
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Multiple futures prediction
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Multi-agent tensor fusion for contextual trajectory prediction
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Social gan: Socially acceptable trajectories with generative adversarial networks
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Convolutional neural network for trajectory prediction
Nishant Nikhil and Brendan Tran Morris · 2018
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The book of why: the new science of cause and effect
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Self-growing spatial graph networks for pedestrian trajectory prediction
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Simaug: Learning robust representations from simulation for trajectory prediction
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It is not the journey but the destination: Endpoint conditioned trajectory prediction
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Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data
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Unbiased scene graph generation from biased training
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Unbiased scene graph generation from biased training
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Causal mediation analysis for interpreting neural nlp: The case of gender bias
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart Shieber · 2020
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Tan Wang, Jianqiang Huang, Hanwang Zhang, and Qianru Sun · 2020
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Spatio-temporal graph transformer networks for pedestrian trajectory prediction
Cunjun Yu, Xiao Ma, Jiawei Ren, Haiyu Zhao, and Shuai Yi · 2020
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Transformer networks for trajectory forecasting
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