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 parametrize 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.
Related Research
-
What Constitutes Good Contrastive Learning in Time-Series Forecasting?
What Constitutes Good Contrastive Learning in Time-Series Forecasting?
C. Zhang, Q. Yan, L. Meng, and T. Sylvain.
Research
-
RBC Borealis at International Conference on Learning Representations (ICLR): Machine Learning for a better financial future
RBC Borealis at International Conference on Learning Representations (ICLR): Machine Learning for a better financial future
Learning And Generalization; Natural Language Processing; Time series Modelling
Research
-
Self-Supervised Time Series Representation Learning with Temporal-Instance Similarity Distillation
Self-Supervised Time Series Representation Learning with Temporal-Instance Similarity Distillation
A. Hajimoradlou, L. Pishdad, F. Tung, and M. Karpusha. Workshop at International Conference on Machine Learning (ICML)
Publications