Continuous-depth Deep Gaussian Processes

The paper proposes a continuous-depth formulation for Deep Gaussian Processes (DGPs), treating depth as a latent continuous evolution rather than a discrete stack. It introduces two instantiations—CDGP, which applies a GP prior directly to the state–depth drift, and FlowDGP, which uses a flow-evolved prior to capture richer depth dependence at lower training cost. Experiments on synthetic, benchmark, and large-scale regression tasks demonstrate that residual DGP improves over plain DGP, while CDGP and FlowDGP offer stronger performance across the studied regimes.

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