Cover Image

A Compact Representation for Bayesian Neural Networks By Removing Permutation Symmetry

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xiao, Tim Z., Liu, Weiyang, Bamler, Robert
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector
by: Zhang, Andi, et al.
Published: (2024)

FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
by: Cinquin, Tristan, et al.
Published: (2024)

Compact Bayesian Neural Networks via pruned MCMC sampling
by: Deo, Ratneel, et al.
Published: (2025)

Verbalized Machine Learning: Revisiting Machine Learning with Language Models
by: Xiao, Tim Z., et al.
Published: (2024)

Understanding Task Representations in Neural Networks via Bayesian Ablation
by: Nam, Andrew, et al.
Published: (2025)

Discovering the Representation Bottleneck of Graph Neural Networks
by: Wu, Fang, et al.
Published: (2022)

Remove Symmetries to Control Model Expressivity and Improve Optimization
by: Ziyin, Liu, et al.
Published: (2024)

Reparameterized LLM Training via Orthogonal Equivalence Transformation
by: Qiu, Zeju, et al.
Published: (2025)

Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling
by: Xiao, Tim Z., et al.
Published: (2025)

Symmetry in Neural Network Parameter Spaces
by: Zhao, Bo, et al.
Published: (2025)

Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection
by: Liu, Rui, et al.
Published: (2025)

Permutation-Invariant Graph Partitioning:How Graph Neural Networks Capture Structural Interactions?
by: Hevapathige, Asela, et al.
Published: (2023)

Full Bayesian Significance Testing for Neural Networks
by: Liu, Zehua, et al.
Published: (2024)

On the Probabilistic Learnability of Compact Neural Network Preimage Bounds
by: Marzari, Luca, et al.
Published: (2025)

Precise Bayesian Neural Networks
by: Brito, Carlos Stein
Published: (2025)

Revisiting Multi-Permutation Equivariance through the Lens of Irreducible Representations
by: Sverdlov, Yonatan, et al.
Published: (2024)

Graph Unlearning: Efficient Node Removal in Graph Neural Networks
by: Guan, Faqian, et al.
Published: (2025)

Graph Neural Networks for Learning Equivariant Representations of Neural Networks
by: Kofinas, Miltiadis, et al.
Published: (2024)

BARNN: A Bayesian Autoregressive and Recurrent Neural Network
by: Coscia, Dario, et al.
Published: (2025)

Efficient Traffic Forecasting on Large-Scale Road Network by Regularized Adaptive Graph Convolution
by: Wu, Kaiqi, et al.
Published: (2025)

From Sorting Algorithms to Scalable Kernels: Bayesian Optimization in High-Dimensional Permutation Spaces
by: Xie, Zikai, et al.
Published: (2025)

Optimizing Sensory Neurons: Nonlinear Attention Mechanisms for Accelerated Convergence in Permutation-Invariant Neural Networks for Reinforcement Learning
by: Muzaffar, Junaid, et al.
Published: (2025)

Incorporating Unlabelled Data into Bayesian Neural Networks
by: Sharma, Mrinank, et al.
Published: (2023)

Stochastic Weight Sharing for Bayesian Neural Networks
by: Lin, Moule, et al.
Published: (2025)

Regularizing Explanations in Bayesian Convolutional Neural Networks
by: Bekkemoen, Yanzhe, et al.
Published: (2021)

Bayesian Neural Networks: A Min-Max Game Framework
by: Hong, Junping, et al.
Published: (2023)

A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data & Overparameterization?
by: Xiao, Tim Z., et al.
Published: (2023)

Learning Interpretable Low-dimensional Representation via Physical Symmetry
by: Liu, Xuanjie, et al.
Published: (2023)

Reducing Storage of Pretrained Neural Networks by Rate-Constrained Quantization and Entropy Coding
by: Conzelmann, Alexander, et al.
Published: (2025)

Tubular Riemannian Laplace Approximations for Bayesian Neural Networks
by: David, Rodrigo Pereira
Published: (2025)

EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks
by: Arbel, Michael, et al.
Published: (2025)

Neural Networks: According to the Principles of Grassmann Algebra
by: Zarezadeh, Z., et al.
Published: (2025)

SemiReward: A General Reward Model for Semi-supervised Learning
by: Li, Siyuan, et al.
Published: (2023)

Learning Compact Boolean Networks
by: Wang, Shengpu, et al.
Published: (2026)

Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural Networks
by: Liu, Xuyuan, et al.
Published: (2024)

Bayesian Neural Network For Personalized Federated Learning Parameter Selection
by: Luo, Mengen, et al.
Published: (2024)

Explaining Bayesian Neural Networks
by: Bykov, Kirill, et al.
Published: (2021)

GraphSparseNet: a Novel Method for Large Scale Traffic Flow Prediction
by: Kong, Weiyang, et al.
Published: (2025)

Fast Inference of Removal-Based Node Influence
by: Li, Weikai, et al.
Published: (2024)

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
by: Lee, Dongwoo, et al.
Published: (2025)