Image de couverture de livre

Three Costs of Amortizing Gaussian Process Inference with Neural Processes

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Young, Robin
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Amortized Variational Inference for Deep Gaussian Processes
par: Meng, Qiuxian, et autres
Publié: (2024)

Characterizing the Representational Capacity of Neural Processes
par: Young, Robin
Publié: (2026)

On the Conditioning Consistency Gap in Conditional Neural Processes
par: Young, Robin
Publié: (2026)

Neural Methods for Amortized Inference
par: Zammit-Mangion, Andrew, et autres
Publié: (2024)

Amortized Network Intervention to Steer the Excitatory Point Processes
par: Song, Zitao, et autres
Publié: (2023)

Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes
par: Feng, Brandon R., et autres
Publié: (2024)

Neural Processes Maintain Calibrated Biomass Estimates Across Spatiotemporal Gaps and Disturbance
par: Young, Robin, et autres
Publié: (2026)

Sparse Gaussian Neural Processes
par: Rochussen, Tommy, et autres
Publié: (2025)

Amortized Factor Inference Networks for Posterior Inference
par: Ko, Joohwan, et autres
Publié: (2026)

Instrumental and Proximal Causal Inference with Gaussian Processes
par: Zhang, Yuqi, et autres
Publié: (2026)

Sparse Orthogonal Variational Inference for Gaussian Processes
par: Shi, Jiaxin, et autres
Publié: (2019)

Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes
par: Xu, Jian, et autres
Publié: (2023)

Amortized Variational Inference: When and Why?
par: Margossian, Charles C., et autres
Publié: (2023)

Bayesian Causal Inference with Gaussian Process Networks
par: Giudice, Enrico, et autres
Publié: (2024)

Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation
par: Sun, Shiyi, et autres
Publié: (2026)

Sequential Inference for Gaussian Processes: A Signal Processing Perspective
par: Waxman, Daniel, et autres
Publié: (2026)

Lightweight Gaussian Process Inference in C++ on Metal and CUDA
par: Fang, Yu-Hsueh
Publié: (2026)

Exact Inference for Continuous-Time Gaussian Process Dynamics
par: Ensinger, Katharina, et autres
Publié: (2023)

Diffusion Bridge Variational Inference for Deep Gaussian Processes
par: Xu, Jian, et autres
Publié: (2025)

Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks
par: Antoran, Javier
Publié: (2024)

Sensitivity-Aware Amortized Bayesian Inference
par: Elsemüller, Lasse, et autres
Publié: (2023)

Unsupervised Continual Learning for Amortized Bayesian Inference
par: Mishra, Aayush, et autres
Publié: (2026)

Learning Decision Trees as Amortized Structure Inference
par: Mahfoud, Mohammed, et autres
Publié: (2025)

Amortized Probabilistic Conditioning for Optimization, Simulation and Inference
par: Chang, Paul E., et autres
Publié: (2024)

Novel Pivoted Cholesky Decompositions for Efficient Gaussian Process Inference
par: de Roos, Filip, et autres
Publié: (2025)

Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference
par: Jiang, Xiaoyu, et autres
Publié: (2024)

Gaussian Process Neural Additive Models
par: Zhang, Wei, et autres
Publié: (2024)

Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks
par: Huch, Easton, et autres
Publié: (2026)

Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation
par: Schmitt, Marvin, et autres
Publié: (2024)

Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
par: Rathore, Pratik, et autres
Publié: (2025)

From Shallow Bayesian Neural Networks to Gaussian Processes: General Convergence, Identifiability and Scalable Inference
par: de Araújo, Gracielle Antunes, et autres
Publié: (2026)

Variational Gaussian Process Diffusion Processes
par: Verma, Prakhar, et autres
Publié: (2023)

Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
par: Wenger, Jonathan, et autres
Publié: (2024)

Instance-Adaptive Parametrization for Amortized Variational Inference
par: Pollastro, Andrea, et autres
Publié: (2026)

From Mice to Trains: Amortized Bayesian Inference on Graph Data
par: Jedhoff, Svenja, et autres
Publié: (2026)

ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
par: Huang, Daolang, et autres
Publié: (2025)

ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems
par: Orozco, Rafael, et autres
Publié: (2024)

Inference of Multiscale Gaussian Graphical Model
par: Sanou, Do Edmond, et autres
Publié: (2022)

Empirical Gaussian Processes
par: Lin, Jihao Andreas, et autres
Publié: (2026)

JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference
par: Bracher, Niels, et autres
Publié: (2025)