Dynamic data modeling, recognition, and synthesis
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Authors
Zhao, Rui
Issue Date
2018-12
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Computer Systems engineering
Alternative Title
Abstract
Finally, we address the challenge of modeling complex dynamics emerging in dynamic data. The source of complexity is two-fold. First, there exist complex interdependencies among different components that generate the dynamic data. Second, there exist long-term temporal dependencies between data samples observed at different times. We propose a Bayesian neural networks (BNN) model that integrates Bayesian modeling with neural networks (NN) to leverage the benefits of both probabilistic and deterministic approaches. Specifically, we use graph convolution to capture the structural dependencies among different components of the dynamic data, whose temporal dynamics are further modeled by recurrent neural networks (RNN) with long short-term memory (LSTM). The entire model is extended to a probabilistic model to better handle the randomness in the dynamic data. A Bayesian inference framework is formulated to perform a classification task, and an adversarial prior is developed to further improve the generalization of the model. We demonstrate the effectiveness of the proposed framework on skeleton-based human action recognition.
Description
December 2018
School of Engineering
School of Engineering
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY