How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs
arXiv:2606.08218v1 Announce Type: new Abstract: Compositional priors describe the generic properties of layered functions in deep Bayesian models, where deep neural networks with random weights are a canonical example.In the wide-network limit, the prior is a Gaussian process with a depth-dependent kernel, and its behaviour as depth grows has been extensively studied through this kernel. Here, we study another case, where each layer itself is a vector valued Gaussian process, and our aim is similarly to understand the limiting behaviour of the prior as depth grows. Previous GP work has establi
This research is part of ongoing efforts to deepen theoretical understanding of deep learning models, particularly as AI systems become more complex and widespread.
Advanced theoretical understanding of AI models can lead to more robust, predictable, and efficient AI systems, impacting their development and deployment.
This research provides a more nuanced theoretical framework for understanding the behavior of deep compositional models, moving beyond previous Gaussian process limits.
- · AI researchers
- · Deep learning developers
- · Theoretical machine learning
Improved theoretical understanding of deep Gaussian processes within AI research.
Potential for designing more stable and explainable deep learning architectures based on new theoretical insights.
This could contribute to the long-term development of more reliable and trustworthy AI systems, expanding their applicability in critical domains.
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