Multiscale generative models: Improving performance of a generative model using feedback from other dependent generative models
Realistic fine-grained multi-agent simulation of real-world complex systems is crucial for many downstream tasks such as reinforcement learning. Recent work has used generative models (GANs in particular) for providing high-fidelity simulation of real-world systems. However, such generative models a...
Saved in:
Main Authors: | CHEN, Changyu, BOSE, Avinandan, CHENG, Shih-Fen, SINHA, Arunesh |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2022
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/6792 https://ink.library.smu.edu.sg/context/sis_research/article/7795/viewcontent/MGM_AAAI22.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
NumGPT: Improving numeracy ability of generative pre-trained models
by: JIN, Zhihua, et al.
Published: (2023) -
Ship-GAN: Generative modeling based maritime traffic simulator
by: BASRUR, Chaithanya, et al.
Published: (2021) -
Choices are not independent: Stackelberg security games with nested quantal response models
by: MAI, Tien, et al.
Published: (2022) -
Learning and Controlling Network Diffusion in Dependent Cascade Models
by: DU, Jiali, et al.
Published: (2015) -
Authentic and insightful use of generative AI
by: Aure, Patrick Adriel H.
Published: (2023)