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• W2955358753 abstract "We propose a novel probabilistic generative model for action sequences. The model is termed the Action Point Process VAE (APP-VAE), a variational auto-encoder that can capture the distribution over the times and categories of action sequences. Modeling the variety of possible action sequences is a challenge, which we show can be addressed via the APP-VAE's use of latent representations and non-linear functions to parameterize distributions over which event is likely to occur next in a sequence and at what time. We empirically validate the efficacy of APP-VAE for modeling action sequences on the MultiTHUMOS and Breakfast datasets." @default.
• W2955358753 created "2019-07-12" @default.
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• W2955358753 date "2019-06-01" @default.
• W2955358753 modified "2023-10-16" @default.
• W2955358753 title "A Variational Auto-Encoder Model for Stochastic Point Processes" @default.
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• W2955358753 doi "https://doi.org/10.1109/cvpr.2019.00328" @default.
• W2955358753 hasPublicationYear "2019" @default.
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