An Analysis of Posterior Collapse, Parameterization and Initialization in Variational Deep Gaussian Processes
arXiv:2606.25882v1 Announce Type: new Abstract: DGPs are probabilistic models with remarkable prediction performance that concatenate GPs across several layers. Exact inference in DGPs is intractable, and variational inference is often used to approximate the posterior with a parametric distribution tuned by minimizing the Kullback-Leibler divergence. Moreover, finding a good VI approximation is challenging. In particular, a problem of VI is posterior collapse, where VI converges to a variational posterior that matches the prior. In variational DGPs, this implies explaining the data as noise.
This research is part of ongoing efforts to address fundamental challenges in variational inference for deep probabilistic models, as AI research continues to push the boundaries of model complexity and reliability.
Improving the robustness and accuracy of variational inference in Deep Gaussian Processes (DGPs) is crucial for developing more reliable and interpretable AI systems, particularly in applications requiring uncertainty quantification.
Better understanding and mitigation of posterior collapse could lead to more stable and performant DGP models, making them more practical for real-world applications where current approximations are often insufficient.
- · AI researchers
- · Deep learning practitioners
- · Developers of probabilistic AI
- · Developers relying on unstable variational inference methods
Research into foundational AI algorithms becomes more sophisticated, leading to incremental improvements in model performance and reliability.
More stable and accurate probabilistic models could enhance decision-making systems in fields like healthcare, finance, or autonomous systems by providing better uncertainty estimates.
The broader adoption of trustworthy AI models could accelerate automation and agentic systems, as their reliability fosters greater societal acceptance.
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Read at arXiv cs.LG