Stochastic Deep Learning: A Probabilistic Framework for Modeling Uncertainty in Structured Temporal Data
随机深度学习：结构化时间数据不确定性建模的概率框架
I propose a novel framework that integrates stochastic differential equations (SDEs) with deep generative models to improve uncertainty quantification in machine learning applications involving structured and temporal data. This approach, termed Stochastic Latent Differential Inference (SLDI), embeds an Itô SDE in the latent space of a variational autoencoder, allowing for flexible, continuous-time modeling of uncertainty while preserving a principled mathematical…