Three Costs of Amortizing Gaussian Process Inference with Neural Processes
A new research paper details three primary costs associated with amortizing Gaussian Process inference using Neural Processes. The study identifies label contamination, an information bottleneck, and amortization error as key factors. The paper provides mathematical bounds for these costs and offers architectural recommendations to improve efficiency and accuracy in this domain. AI
IMPACT Characterizes the trade-offs in amortizing Gaussian Process inference, offering insights for researchers developing more efficient probabilistic models.