Difference between revisions of "Generative Approach for Multivariate Signals"
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− | |Summary=. | + | |Summary=The topic focuses on generative models (VAE) for CAN-bus data and investigating the representation learning capabilities of such techniques |
− | |Supervisor= | + | |Keywords=VAE, Time-series data, Streaming data, MAR |
− | |Status= | + | |TimeFrame=2021 Fall - 2022 Summer |
+ | |References=https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf | ||
+ | |||
+ | https://openreview.net/pdf?id=Sy2fzU9gl | ||
+ | |||
+ | https://www.sciencedirect.com/science/article/pii/S092658051930367X | ||
+ | |Supervisor=Kunru Chen, Abdallah Alabdallah, Thorsteinn Rögnvaldsson | ||
+ | |Level=Master | ||
+ | |Status=Open | ||
}} | }} |
Latest revision as of 16:27, 12 October 2023
Title | Generative Approach for Multivariate Signals |
---|---|
Summary | The topic focuses on generative models (VAE) for CAN-bus data and investigating the representation learning capabilities of such techniques |
Keywords | VAE, Time-series data, Streaming data, MAR |
TimeFrame | 2021 Fall - 2022 Summer |
References | https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf
https://openreview.net/pdf?id=Sy2fzU9gl https://www.sciencedirect.com/science/article/pii/S092658051930367X |
Prerequisites | |
Author | |
Supervisor | Kunru Chen, Abdallah Alabdallah, Thorsteinn Rögnvaldsson |
Level | Master |
Status | Open |