Learning to Simulate Sequentially Generated Data via Neural Networks and Wasserstein Training
Published in ACM Transactions on Modeling and Computer Simulation, 2023
This paper presents a novel approach for learning to simulate sequentially generated data using neural networks trained with Wasserstein distance. The method provides robust simulation capabilities for complex sequential data patterns.
Authors: Tingyu Zhu, Haoyu Liu, Zeyu Zheng
Published in ACM Transactions on Modeling and Computer Simulation, Volume 33, Issue 3, 2023, pp. 1-34.