GNet: A scalable and flexible Gaussian process network with nonparametric neurons

arXiv:2607.10735 · stat.ME, stat.CO, stat.ML · Submitted 2026-07-12 · Read on arXiv

stat.ME, stat.CO, stat.ML

Submitted: 2026-07-12

Updated: 2026-09-21

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 9× in this paper · explained in 1 episode
• Adam — 5× in this paper · explained in 3 episodes
• Preconditioner — 4× in this paper · explained in 2 episodes
• Predictive Modeling — 4× in this paper · explained in 3 episodes
• Uncertainty Quantification — 3× in this paper · explained in 9 episodes
• Core — 2× in this paper · explained in 2 episodes
• Empirical Coverage — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Transformers — 2× in this paper · explained in 2 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Energy Efficiency — 1× in this paper · explained in 4 episodes
• Gaussian Processes (GPs) — 1× in this paper · explained in 3 episodes
• Multilayer Perceptron (MLP) — 1× in this paper · explained in 2 episodes

Sources

• Learning Activation Functions to Improve Deep Neural Networks
• Adam: A Method for Stochastic Optimization
• All Emulators are Wrong, Many are Useful, and Some are More Useful Than Others: A Reproducible Comparison of Computer Model Surrogates
• Gaussian Process Neurons Learn Stochastic Activation Functions

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