Structured Partial Stochasticity in Bayesian Neural Networks
Fuente:
arXiv
Saved in:
| Main Author: | Rochussen, Tommy |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Amortising Inference and Meta-Learning Priors in Neural Networks
by: Rochussen, Tommy, et al.
Published: (2026)
by: Rochussen, Tommy, et al.
Published: (2026)
Sparse Gaussian Neural Processes
by: Rochussen, Tommy, et al.
Published: (2025)
by: Rochussen, Tommy, et al.
Published: (2025)
Incremental Transformer Neural Processes
by: Mortimer, Philip, et al.
Published: (2026)
by: Mortimer, Philip, et al.
Published: (2026)
Partially Stochastic Infinitely Deep Bayesian Neural Networks
by: Calvo-Ordonez, Sergio, et al.
Published: (2024)
by: Calvo-Ordonez, Sergio, et al.
Published: (2024)
Bridging GANs and Bayesian Neural Networks via Partial Stochasticity
by: Filippone, Maurizio, et al.
Published: (2025)
by: Filippone, Maurizio, et al.
Published: (2025)
Functional Stochastic Gradient MCMC for Bayesian Neural Networks
by: Wu, Mengjing, et al.
Published: (2024)
by: Wu, Mengjing, et al.
Published: (2024)
Stochastic Weight Sharing for Bayesian Neural Networks
by: Lin, Moule, et al.
Published: (2025)
by: Lin, Moule, et al.
Published: (2025)
Partial Trace-Class Bayesian Neural Networks
by: Carter, Arran, et al.
Published: (2025)
by: Carter, Arran, et al.
Published: (2025)
Learning Solutions of Stochastic Optimization Problems with Bayesian Neural Networks
by: Lahoud, Alan A., et al.
Published: (2024)
by: Lahoud, Alan A., et al.
Published: (2024)
Adaptive Stepsizing for Stochastic Gradient Langevin Dynamics in Bayesian Neural Networks
by: Rajpal, Rajit, et al.
Published: (2025)
by: Rajpal, Rajit, et al.
Published: (2025)
Linear Noise Approximation Assisted Bayesian Inference on Mechanistic Model of Partially Observed Stochastic Reaction Network
by: Xu, Wandi, et al.
Published: (2024)
by: Xu, Wandi, et al.
Published: (2024)
Graph Structure Learning with Interpretable Bayesian Neural Networks
by: Wasserman, Max, et al.
Published: (2024)
by: Wasserman, Max, et al.
Published: (2024)
Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations
by: Shi, Dai, et al.
Published: (2026)
by: Shi, Dai, et al.
Published: (2026)
Fast Bayesian Optimization of Function Networks with Partial Evaluations
by: Buathong, Poompol, et al.
Published: (2025)
by: Buathong, Poompol, et al.
Published: (2025)
Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networks
by: Millard, Andrew, et al.
Published: (2025)
by: Millard, Andrew, et al.
Published: (2025)
Bayesian Optimization of Function Networks with Partial Evaluations
by: Buathong, Poompol, et al.
Published: (2023)
by: Buathong, Poompol, et al.
Published: (2023)
Spatial Bayesian Neural Networks
by: Zammit-Mangion, Andrew, et al.
Published: (2023)
by: Zammit-Mangion, Andrew, et al.
Published: (2023)
Partially Observable Stochastic Games with Neural Perception Mechanisms
by: Yan, Rui, et al.
Published: (2023)
by: Yan, Rui, et al.
Published: (2023)
Chaos into Order: Neural Framework for Expected Value Estimation of Stochastic Partial Differential Equations
by: Pétursson, Ísak, et al.
Published: (2025)
by: Pétursson, Ísak, et al.
Published: (2025)
Wiener Chaos Expansion based Neural Operator for Singular Stochastic Partial Differential Equations
by: Shi, Dai, et al.
Published: (2026)
by: Shi, Dai, et al.
Published: (2026)
Singular Bayesian Neural Networks
by: Toure, Mame Diarra, et al.
Published: (2026)
by: Toure, Mame Diarra, et al.
Published: (2026)
Bayesian Neural Networks: An Introduction and Survey
by: Goan, Ethan, et al.
Published: (2020)
by: Goan, Ethan, et al.
Published: (2020)
Data Subsampling for Bayesian Neural Networks
by: Kawasaki, Eiji, et al.
Published: (2022)
by: Kawasaki, Eiji, et al.
Published: (2022)
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
by: Li, Yucen Lily, et al.
Published: (2023)
by: Li, Yucen Lily, et al.
Published: (2023)
Random-Set Graph Neural Networks
by: Woodley, Tommy, et al.
Published: (2026)
by: Woodley, Tommy, et al.
Published: (2026)
Uncertainty Estimation on Graphs with Structure Informed Stochastic Partial Differential Equations
by: Xu, Fred, et al.
Published: (2025)
by: Xu, Fred, et al.
Published: (2025)
Structural Dimension Reduction in Bayesian Networks
by: Heng, Pei, et al.
Published: (2026)
by: Heng, Pei, et al.
Published: (2026)
Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations
by: Huynh, Phuoc-Toan, et al.
Published: (2026)
by: Huynh, Phuoc-Toan, et al.
Published: (2026)
BPINN-EM-Post: Bayesian Physics-Informed Neural Network based Stochastic Electromigration Damage Analysis in the Post-void Phase
by: Lamichhane, Subed, et al.
Published: (2025)
by: Lamichhane, Subed, et al.
Published: (2025)
Risk-Averse Certification of Bayesian Neural Networks
by: Zhang, Xiyue, et al.
Published: (2024)
by: Zhang, Xiyue, et al.
Published: (2024)
Bayesian Neural Networks with Domain Knowledge Priors
by: Sam, Dylan, et al.
Published: (2024)
by: Sam, Dylan, et al.
Published: (2024)
Improved Depth Estimation of Bayesian Neural Networks
by: van Erp, Bart, et al.
Published: (2024)
by: van Erp, Bart, et al.
Published: (2024)
Precise Bayesian Neural Networks
by: Brito, Carlos Stein
Published: (2025)
by: Brito, Carlos Stein
Published: (2025)
Bayesian Neighborhood Adaptation for Graph Neural Networks
by: Regmi, Paribesh, et al.
Published: (2026)
by: Regmi, Paribesh, et al.
Published: (2026)
Bayesian Neural Networks for Functional ANOVA model
by: Park, Seokhun, et al.
Published: (2025)
by: Park, Seokhun, et al.
Published: (2025)
Partially Observable Gaussian Process Network and Doubly Stochastic Variational Inference
by: Kiroriwal, Saksham, et al.
Published: (2025)
by: Kiroriwal, Saksham, et al.
Published: (2025)
Bayesian Neural Networks for Macroeconomic Analysis
by: Hauzenberger, Niko, et al.
Published: (2022)
by: Hauzenberger, Niko, et al.
Published: (2022)
Randomized Confidence Bounds for Stochastic Partial Monitoring
by: Heuillet, Maxime, et al.
Published: (2024)
by: Heuillet, Maxime, et al.
Published: (2024)
Stochastic Gradient Descent for Two-layer Neural Networks
by: Cao, Dinghao, et al.
Published: (2024)
by: Cao, Dinghao, et al.
Published: (2024)
Variational Stochastic Gradient Descent for Deep Neural Networks
by: Chen, Haotian, et al.
Published: (2024)
by: Chen, Haotian, et al.
Published: (2024)
Similar Items
-
Amortising Inference and Meta-Learning Priors in Neural Networks
by: Rochussen, Tommy, et al.
Published: (2026) -
Sparse Gaussian Neural Processes
by: Rochussen, Tommy, et al.
Published: (2025) -
Incremental Transformer Neural Processes
by: Mortimer, Philip, et al.
Published: (2026) -
Partially Stochastic Infinitely Deep Bayesian Neural Networks
by: Calvo-Ordonez, Sergio, et al.
Published: (2024) -
Bridging GANs and Bayesian Neural Networks via Partial Stochasticity
by: Filippone, Maurizio, et al.
Published: (2025)