Neural processes assumed clean inputs: DnNP folds denoising into meta-learning and cuts PM2.5 interpolation RMSE by up to 30%
Standard neural processes treat inputs as noise-free observations, so sensor measurement error degrades both predictions and uncertainty estimates. DnNP makes the clean input a latent variable inside the neural-process framework and trains a denoiser jointly with meta-learning through variational inference, predicting directly from noisy inputs. On PM2.5 spatial interpolation from 34 air-quality sensors in Brookline, Massachusetts, DnNP reduces RMSE by up to 30% and NLL by up to 73% relative to standard neural processes.