Image de couverture de livre

Stochastic Weight Sharing for Bayesian Neural Networks

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Lin, Moule, Guan, Shuhao, Jing, Weipeng, Botterweck, Goetz, Patane, Andrea
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Flow-Induced Diagonal Gaussian Processes
par: Lin, Moule, et autres
Publié: (2025)

Bayesian-LoRA: Probabilistic Low-Rank Adaptation of Large Language Models
par: Lin, Moule, et autres
Publié: (2026)

On the Robustness of Bayesian Neural Networks to Adversarial Attacks
par: Bortolussi, Luca, et autres
Publié: (2022)

Neural Architecture Search: Two Constant Shared Weights Initialisations
par: Gracheva, Ekaterina
Publié: (2023)

Optimized Vectorizing of Building Structures with Switch: High-Efficiency Convolutional Channel-Switch Hybridization Strategy
par: Lin, Moule, et autres
Publié: (2023)

Precise Bayesian Neural Networks
par: Brito, Carlos Stein
Publié: (2025)

Diffusion-Based Neural Network Weights Generation
par: Soro, Bedionita, et autres
Publié: (2024)

Full Bayesian Significance Testing for Neural Networks
par: Liu, Zehua, et autres
Publié: (2024)

Incorporating Unlabelled Data into Bayesian Neural Networks
par: Sharma, Mrinank, et autres
Publié: (2023)

Regularizing Explanations in Bayesian Convolutional Neural Networks
par: Bekkemoen, Yanzhe, et autres
Publié: (2021)

Assessing Simplification Levels in Neural Networks: The Impact of Hyperparameter Configurations on Complexity and Sensitivity
par: Guan, Huixin
Publié: (2024)

Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations
par: Xue, Kaiwen, et autres
Publié: (2024)

BARNN: A Bayesian Autoregressive and Recurrent Neural Network
par: Coscia, Dario, et autres
Publié: (2025)

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

Artificial Geographically Weighted Neural Network: A Novel Framework for Spatial Analysis with Geographically Weighted Layers
par: Cao, Jianfei, et autres
Publié: (2025)

Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
par: Xu, Jian, et autres
Publié: (2024)

Understanding the Generalization of Stochastic Gradient Adam in Learning Neural Networks
par: Tang, Xuan, et autres
Publié: (2025)

Robust Weight Initialization for Tanh Neural Networks with Fixed Point Analysis
par: Lee, Hyunwoo, et autres
Publié: (2024)

Understanding Task Representations in Neural Networks via Bayesian Ablation
par: Nam, Andrew, et autres
Publié: (2025)

Compact Bayesian Neural Networks via pruned MCMC sampling
par: Deo, Ratneel, et autres
Publié: (2025)

Bayesian Neural Network For Personalized Federated Learning Parameter Selection
par: Luo, Mengen, et autres
Publié: (2024)

Bayesian Neural Networks: A Min-Max Game Framework
par: Hong, Junping, et autres
Publié: (2023)

Rethinking Spiking Neural Networks from an Ensemble Learning Perspective
par: Ding, Yongqi, et autres
Publié: (2025)

Partition Tree Weighting for Non-Stationary Stochastic Bandits
par: Veness, Joel, et autres
Publié: (2025)

Has the Deep Neural Network learned the Stochastic Process? An Evaluation Viewpoint
par: Kumar, Harshit, et autres
Publié: (2024)

OSDTW: Optimal Shared Depth and Task Weighting for Long-Tailed Recognition
par: Chu, Chang, et autres
Publié: (2026)

Teasing Apart Architecture and Initial Weights as Sources of Inductive Bias in Neural Networks
par: Bencomo, Gianluca, et autres
Publié: (2025)

Neural Network-based Information Set Weighting for Playing Reconnaissance Blind Chess
par: Bertram, Timo, et autres
Publié: (2024)

NNiT: Width-Agnostic Neural Network Generation with Structurally Aligned Weight Spaces
par: Kim, Jiwoo, et autres
Publié: (2026)

Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
par: Lee, Dongwoo, et autres
Publié: (2025)

From Overfitting to Reliability: Introducing the Hierarchical Approximate Bayesian Neural Network
par: Amirkhanian, Hayk, et autres
Publié: (2025)

A Compact Representation for Bayesian Neural Networks By Removing Permutation Symmetry
par: Xiao, Tim Z., et autres
Publié: (2023)

Interpretable Neural Networks with Random Constructive Algorithm
par: Nan, Jing, et autres
Publié: (2023)

Stochastic KV Routing: Enabling Adaptive Depth-Wise Cache Sharing
par: Filippova, Anastasiia, et autres
Publié: (2026)

Neural Bayesian Filtering
par: Solinas, Christopher, et autres
Publié: (2025)

DeltaLLM: Compress LLMs with Low-Rank Deltas between Shared Weights
par: Mikaelyan, Liana, et autres
Publié: (2025)

Weight-of-Thought Reasoning: Exploring Neural Network Weights for Enhanced LLM Reasoning
par: Punjwani, Saif, et autres
Publié: (2025)

On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysis
par: Guan, Junyi, et autres
Publié: (2025)

Native Fortran Implementation of TensorFlow-Trained Deep and Bayesian Neural Networks
par: Furlong, Aidan, et autres
Publié: (2025)

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