Semi-Supervised Generative Models for Multi-Agent Trajectories
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Advances in Neural Information Processing Systems 35: 36th Conference on Neural Information Processing Systems (NeurIPS 2022). ed. / S. Koyejo; S. Mohamed; A. Agarwal; D. Belgrave; K. Cho; A. Oh. Vol. 48 Red Hook: Curran Associates, 2022. p. 37267-37281 (Advances in Neural Information Processing Systems; Vol. 35).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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RIS
TY - CHAP
T1 - Semi-Supervised Generative Models for Multi-Agent Trajectories
AU - Brefeld, Ulf
AU - Fassmeyer, Dennis
AU - Fassmeyer, Pascal
N1 - Conference code: 36
PY - 2022
Y1 - 2022
N2 - Analyzing the spatiotemporal behavior of multiple agents is of great interest to many communities. Existing probabilistic models in this realm are formalized either in an unsupervised framework, where the latent space is described by discrete or continuous variables, or in a supervised framework, where weakly preserved labels add explicit information to continuous latent representations. To overcome inherent limitations, we propose a novel objective function for processing multi-agent trajectories based on semi-supervised variational autoencoders, where equivariance and interaction of agents are captured via customized graph networks. The resulting architecture disentangles discrete and continuous latent effects and provides a natural solution for injecting expensive domain knowledge into interactive sequential systems. Empirically, our model not only outperforms various state-of-the-art baselines in trajectory forecasting, but also learns to effectively leverage unsupervised multi-agent sequences for classification tasks on interactive real-world sports datasets.
AB - Analyzing the spatiotemporal behavior of multiple agents is of great interest to many communities. Existing probabilistic models in this realm are formalized either in an unsupervised framework, where the latent space is described by discrete or continuous variables, or in a supervised framework, where weakly preserved labels add explicit information to continuous latent representations. To overcome inherent limitations, we propose a novel objective function for processing multi-agent trajectories based on semi-supervised variational autoencoders, where equivariance and interaction of agents are captured via customized graph networks. The resulting architecture disentangles discrete and continuous latent effects and provides a natural solution for injecting expensive domain knowledge into interactive sequential systems. Empirically, our model not only outperforms various state-of-the-art baselines in trajectory forecasting, but also learns to effectively leverage unsupervised multi-agent sequences for classification tasks on interactive real-world sports datasets.
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85163202847&partnerID=8YFLogxK
UR - https://www.proceedings.com/content/068/068431webtoc.pdf
M3 - Article in conference proceedings
AN - SCOPUS:85163202847
SN - 978-1-7138-7108-8
VL - 48
T3 - Advances in Neural Information Processing Systems
SP - 37267
EP - 37281
BT - Advances in Neural Information Processing Systems 35
A2 - Koyejo, S.
A2 - Mohamed, S.
A2 - Agarwal, A.
A2 - Belgrave, D.
A2 - Cho, K.
A2 - Oh, A.
PB - Curran Associates
CY - Red Hook
T2 - 36th Conference on Neural Information Processing Systems - NeurIPS 2022
Y2 - 28 November 2022 through 9 December 2022
ER -