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Explore the impact of loss functions and data set sizes on neural network performance in diffusion prediction models.
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The results section analyzes the performance of neural network models trained on different loss functions and data set sizes for diffusion prediction. It highlights the significance of data set size in model performance, discusses the effects of various loss functions, and evaluates model stability and fluctuations. Additionally, it delves into inference prediction and the optimal model configurations for different numbers of sources in the lattice, suggesting insights into data set curation.