variational-optimization

Automatic Structured Variational Inference

Introduction: What is ASVI? Automatic Structured Variational Inference (ASVI) is a method for constructing structured variational families for probabilistic models. It is a fully automated process that is inspired by the closed-form update in conjugate Bayesian models. The goal of ASVI is to create convex-update families that can capture complex statistical dependencies to produce more accurate results. By doing this, ASVI can help researchers and data scientists create better models that can b

Residual Normal Distribution

Understanding Residual Normal Distributions Residual Normal Distributions are an important tool for optimizing Variational Autoencoders (VAEs). In simple terms, VAEs are neural networks that aim to learn the underlying structure of a dataset and generate new examples that belong to the same category. Residual Normal Distributions help the VAE optimization process by preventing the network from entering an unstable region, which can occur due to sharp gradients when the encoder and decoder produ

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