Image de couverture de livre

Symmetry in Neural Network Parameter Spaces

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zhao, Bo, Walters, Robin, Yu, Rose
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

The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
par: Lim, Derek, et autres
Publié: (2024)

Understanding Mode Connectivity via Parameter Space Symmetry
par: Zhao, Bo, et autres
Publié: (2025)

Improving Convergence and Generalization Using Parameter Symmetries
par: Zhao, Bo, et autres
Publié: (2023)

Latent Space Symmetry Discovery
par: Yang, Jianke, et autres
Publié: (2023)

Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution
par: Wang, Rui, et autres
Publié: (2023)

CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks
par: Rao, Xuan, et autres
Publié: (2022)

Leveraging Parameter Space Symmetries for Reasoning Skill Transfer in LLMs
par: Horoi, Stefan, et autres
Publié: (2025)

Denoising Hamiltonian Network for Physical Reasoning
par: Deng, Congyue, et autres
Publié: (2025)

Neural Point Process for Learning Spatiotemporal Event Dynamics
par: Zhou, Zihao, et autres
Publié: (2021)

Flow to Learn: Flow Matching on Neural Network Parameters
par: Saragih, Daniel, et autres
Publié: (2025)

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

Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters
par: Qing, Yong, et autres
Publié: (2023)

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

AI Epidemiology: achieving explainable AI through expert oversight patterns
par: Tempest-Walters, Kit
Publié: (2025)

Developing Distance-Aware, and Evident Uncertainty Quantification in Dynamic Physics-Constrained Neural Networks for Robust Bearing Degradation Estimation
par: Razzaq, Waleed, et autres
Publié: (2025)

MatrixNet: Learning over symmetry groups using learned group representations
par: Laird, Lucas, et autres
Publié: (2025)

SBS: Enhancing Parameter-Efficiency of Neural Representations for Neural Networks via Spectral Bias Suppression
par: Xie, Qihu, et autres
Publié: (2025)

Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset
par: Galashov, Alexandre, et autres
Publié: (2024)

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining
par: Vaska, Nathan, et autres
Publié: (2025)

Neural Network Parameter-optimization of Gaussian pmDAGs
par: Saremi, Mehrzad
Publié: (2023)

DIM: Enforcing Domain-Informed Monotonicity in Deep Neural Networks
par: Salim, Joshua, et autres
Publié: (2025)

NdLinear: Preserving Multi-Dimensional Structure for Parameter-Efficient Neural Networks
par: Reneau, Alex, et autres
Publié: (2025)

Scaling Recurrent Neural Networks to a Billion Parameters with Zero-Order Optimization
par: Chaubard, Francois, et autres
Publié: (2025)

Symmetry-Aware Graph Metanetwork Autoencoders: Model Merging through Parameter Canonicalization
par: Boufalis, Odysseas, et autres
Publié: (2025)

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
par: James, Zachary, et autres
Publié: (2025)

Learning to Explore with Parameter-Space Noise: A Deep Dive into Parameter-Space Noise for Reinforcement Learning with Verifiable Rewards
par: Bai, Bizhe, et autres
Publié: (2026)

Reimagining Parameter Space Exploration with Diffusion Models
par: Zhang, Lijun, et autres
Publié: (2025)

Symmetry-Informed Governing Equation Discovery
par: Yang, Jianke, et autres
Publié: (2024)

The Role of Symmetry in Optimizing Overparameterized Networks
par: Sareen, Kusha, et autres
Publié: (2026)

Closed-Form Interpretation of Neural Network Latent Spaces with Symbolic Gradients
par: Wetzel, Sebastian J., et autres
Publié: (2024)

Symmetry in the Wild: The Role of Equivariance in Neural Fluid Surrogates
par: Rygiel, Patryk, et autres
Publié: (2026)

DNAD: Differentiable Neural Architecture Distillation
par: Rao, Xuan, et autres
Publié: (2025)

Artificial Intelligence-Driven Network-on-Chip Design Space Exploration: Neural Network Architectures for Design
par: N, Amogh Anshu, et autres
Publié: (2025)

Magnitude-Modulated Equivariant Adapter for Parameter-Efficient Fine-Tuning of Equivariant Graph Neural Networks
par: Jin, Dian, et autres
Publié: (2025)

From Parameters to Behaviors: Unsupervised Compression of the Policy Space
par: Tenedini, Davide, et autres
Publié: (2025)

Multi-Objective Neural Architecture Search by Learning Search Space Partitions
par: Zhao, Yiyang, et autres
Publié: (2024)

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

Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease
par: Kashif, Muhammad, et autres
Publié: (2026)

The Forgotten Shield: Safety Grafting in Parameter-Space for Medical MLLMs
par: Zhao, Jiale, et autres
Publié: (2025)

Improving Learning to Optimize Using Parameter Symmetries
par: Zamir, Guy, et autres
Publié: (2025)