Amortising Inference and Meta-Learning Priors in Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Rochussen, Tommy, Fortuin, Vincent |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Sparse Gaussian Neural Processes
by: Rochussen, Tommy, et al.
Published: (2025)
by: Rochussen, Tommy, et al.
Published: (2025)
Structured Partial Stochasticity in Bayesian Neural Networks
by: Rochussen, Tommy
Published: (2024)
by: Rochussen, Tommy
Published: (2024)
Incremental Transformer Neural Processes
by: Mortimer, Philip, et al.
Published: (2026)
by: Mortimer, Philip, et al.
Published: (2026)
Stein Variational Newton Neural Network Ensembles
by: Flöge, Klemens, et al.
Published: (2024)
by: Flöge, Klemens, et al.
Published: (2024)
Compute Optimal Inference and Provable Amortisation Gap in Sparse Autoencoders
by: O'Neill, Charles, et al.
Published: (2024)
by: O'Neill, Charles, et al.
Published: (2024)
Can Transformers Learn Full Bayesian Inference in Context?
by: Reuter, Arik, et al.
Published: (2025)
by: Reuter, Arik, et al.
Published: (2025)
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
by: Cinquin, Tristan, et al.
Published: (2024)
by: Cinquin, Tristan, et al.
Published: (2024)
Incorporating Unlabelled Data into Bayesian Neural Networks
by: Sharma, Mrinank, et al.
Published: (2023)
by: Sharma, Mrinank, et al.
Published: (2023)
ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods
by: Kmicikiewicz, Michal, et al.
Published: (2025)
by: Kmicikiewicz, Michal, et al.
Published: (2025)
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood
by: Dhahri, Rayen, et al.
Published: (2024)
by: Dhahri, Rayen, et al.
Published: (2024)
In-Context Function Learning in Large Language Models
by: Akata, Elif, et al.
Published: (2026)
by: Akata, Elif, et al.
Published: (2026)
Improving Neural Additive Models with Bayesian Principles
by: Bouchiat, Kouroche, et al.
Published: (2023)
by: Bouchiat, Kouroche, et al.
Published: (2023)
Standard Acquisition Is Sufficient for Asynchronous Bayesian Optimization
by: Riegler, Ben, et al.
Published: (2026)
by: Riegler, Ben, et al.
Published: (2026)
Amortised and provably-robust simulation-based inference
by: Bharti, Ayush, et al.
Published: (2026)
by: Bharti, Ayush, et al.
Published: (2026)
Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation
by: Paul, Richard D., et al.
Published: (2024)
by: Paul, Richard D., et al.
Published: (2024)
On the Effect of Regularization on Nonparametric Mean-Variance Regression
by: Wong-Toi, Eliot, et al.
Published: (2025)
by: Wong-Toi, Eliot, et al.
Published: (2025)
Understanding Pathologies of Deep Heteroskedastic Regression
by: Wong-Toi, Eliot, et al.
Published: (2023)
by: Wong-Toi, Eliot, et al.
Published: (2023)
Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information
by: Sergeev, Fedor, et al.
Published: (2024)
by: Sergeev, Fedor, et al.
Published: (2024)
Inference in Spreading Processes with Neural-Network Priors
by: Ghio, Davide, et al.
Published: (2025)
by: Ghio, Davide, et al.
Published: (2025)
From Regression to Inference: Meta-Learning Predictors for Neural Architecture Search
by: Deng, Liping, et al.
Published: (2026)
by: Deng, Liping, et al.
Published: (2026)
Distilling Symbolic Priors for Concept Learning into Neural Networks
by: Marinescu, Ioana, et al.
Published: (2024)
by: Marinescu, Ioana, et al.
Published: (2024)
DeepRV: Accelerating Spatiotemporal Inference with Pre-trained Neural Priors
by: Navott, Jhonathan, et al.
Published: (2025)
by: Navott, Jhonathan, et al.
Published: (2025)
Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference
by: Melnychuk, Valentyn, et al.
Published: (2026)
by: Melnychuk, Valentyn, et al.
Published: (2026)
Bayesian Neural Networks with Domain Knowledge Priors
by: Sam, Dylan, et al.
Published: (2024)
by: Sam, Dylan, et al.
Published: (2024)
Prior-Aligned Meta-RL: Thompson Sampling with Learned Priors and Guarantees in Finite-Horizon MDPs
by: Zhou, Runlin, et al.
Published: (2025)
by: Zhou, Runlin, et al.
Published: (2025)
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks
by: Schnaus, Dominik, et al.
Published: (2023)
by: Schnaus, Dominik, et al.
Published: (2023)
Meta-Learning for Neural Network-based Temporal Point Processes
by: Takimoto, Yoshiaki, et al.
Published: (2024)
by: Takimoto, Yoshiaki, et al.
Published: (2024)
How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?
by: Kristiadi, Agustinus, et al.
Published: (2024)
by: Kristiadi, Agustinus, et al.
Published: (2024)
Foundation Models for Causal Inference via Prior-Data Fitted Networks
by: Ma, Yuchen, et al.
Published: (2025)
by: Ma, Yuchen, et al.
Published: (2025)
BALI: Learning Neural Networks via Bayesian Layerwise Inference
by: Kurle, Richard, et al.
Published: (2024)
by: Kurle, Richard, et al.
Published: (2024)
On the Interplay of Priors and Overparametrization in Bayesian Neural Network Posteriors
by: Kobialka, Julius, et al.
Published: (2026)
by: Kobialka, Julius, et al.
Published: (2026)
Implicit Generative Prior for Bayesian Neural Networks
by: Liu, Yijia, et al.
Published: (2024)
by: Liu, Yijia, et al.
Published: (2024)
Random-Set Graph Neural Networks
by: Woodley, Tommy, et al.
Published: (2026)
by: Woodley, Tommy, et al.
Published: (2026)
Structure Maintained Representation Learning Neural Network for Causal Inference
by: Sun, Yang, et al.
Published: (2025)
by: Sun, Yang, et al.
Published: (2025)
PriorGuide: Test-Time Prior Adaptation for Simulation-Based Inference
by: Yang, Yang, et al.
Published: (2025)
by: Yang, Yang, et al.
Published: (2025)
Prior-Guided Multi-Omic Transformers for Single-Cell Gene Regulatory Network Inference
by: Xu, Tianyang, et al.
Published: (2026)
by: Xu, Tianyang, et al.
Published: (2026)
Decoupled-Value Attention for Prior-Data Fitted Networks: GP Inference for Physical Equations
by: Sharma, Kaustubh, et al.
Published: (2025)
by: Sharma, Kaustubh, et al.
Published: (2025)
Adversarial Attacks on Graph Neural Networks via Meta Learning
by: Zügner, Daniel, et al.
Published: (2019)
by: Zügner, Daniel, et al.
Published: (2019)
Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN
by: Regol, Florence, et al.
Published: (2023)
by: Regol, Florence, et al.
Published: (2023)
Meta-Learning Neural Procedural Biases
by: Raymond, Christian, et al.
Published: (2024)
by: Raymond, Christian, et al.
Published: (2024)
Similar Items
-
Sparse Gaussian Neural Processes
by: Rochussen, Tommy, et al.
Published: (2025) -
Structured Partial Stochasticity in Bayesian Neural Networks
by: Rochussen, Tommy
Published: (2024) -
Incremental Transformer Neural Processes
by: Mortimer, Philip, et al.
Published: (2026) -
Stein Variational Newton Neural Network Ensembles
by: Flöge, Klemens, et al.
Published: (2024) -
Compute Optimal Inference and Provable Amortisation Gap in Sparse Autoencoders
by: O'Neill, Charles, et al.
Published: (2024)