Transcription of Time-series Generative Adversarial Networks
{{id}} {{{paragraph}}}
Example: marketing
Time-series Generative Adversarial Networks
Time-series Generative Adversarial NetworksJinsung Yoon University of California, Los Angeles, Jarrett University of Cambridge, van der SchaarUniversity of Cambridge, UKUniversity of California, Los Angeles, USAAlan Turing Institute, good Generative model for Time-series data should preservetemporal dynamics,in the sense that new sequences respect the original relationships between variablesacross time. Existing methods that bring Generative Adversarial Networks (GANs)into the sequential setting do not adequately attend to the temporal correlationsunique to Time-series data. At the same time, supervised models for sequenceprediction which allow finer control over network dynamics are inherentlydeterministic.
A good generative model for time-series data should preserve temporal dynamics, in the sense that new sequences respect the original relationships between variables across time. Existing methods that bring generative adversarial networks (GANs) into the sequential setting do not adequately attend to the temporal correlations unique to time ...
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document:
Related search queries
Generative adversarial networks, Networks, Adversarial, Deep Learning on Graphs, Wasserstein Generative Adversarial Networks, Self-Attention Generative Adversarial Networks, Generative adversarial, Generative, NANODEGREE PROGRAM SYLLABUS Deep Learning, Adversar-ial, Generative Adversarial Imitation Learning