Training-Free Cross-Architecture Merging for Graph Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Bhattacharya, Rishabh, Kalsariya, Vikaskumar, Manwani, Naresh |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
EdgeMask-DG*: Learning Domain-Invariant Graph Structures via Adversarial Edge Masking
by: Bhattacharya, Rishabh, et al.
Published: (2026)
by: Bhattacharya, Rishabh, et al.
Published: (2026)
Achieving Fair PCA Using Joint Eigenvalue Decomposition
by: Rathore, Vidhi, et al.
Published: (2025)
by: Rathore, Vidhi, et al.
Published: (2025)
DFORD: Directional Feedback based Online Ordinal Regression Learning
by: Manwani, Naresh, et al.
Published: (2025)
by: Manwani, Naresh, et al.
Published: (2025)
Towards Calibrated Losses for Adversarial Robust Reject Option Classification
by: Shah, Vrund, et al.
Published: (2024)
by: Shah, Vrund, et al.
Published: (2024)
Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning
by: Gayen, Jayadratha, et al.
Published: (2025)
by: Gayen, Jayadratha, et al.
Published: (2025)
Pseudo-labelling meets Label Smoothing for Noisy Partial Label Learning
by: Saravanan, Darshana, et al.
Published: (2024)
by: Saravanan, Darshana, et al.
Published: (2024)
Node Classification With Integrated Reject Option
by: Bhaskar, Uday, et al.
Published: (2024)
by: Bhaskar, Uday, et al.
Published: (2024)
ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis
by: Manatkar, Abhijit, et al.
Published: (2024)
by: Manatkar, Abhijit, et al.
Published: (2024)
Optimal Strategies for Federated Learning Maintaining Client Privacy
by: Bhaskar, Uday, et al.
Published: (2025)
by: Bhaskar, Uday, et al.
Published: (2025)
MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration
by: Bhattacharya, Rishabh, et al.
Published: (2025)
by: Bhattacharya, Rishabh, et al.
Published: (2025)
Robustifying and Boosting Training-Free Neural Architecture Search
by: He, Zhenfeng, et al.
Published: (2024)
by: He, Zhenfeng, et al.
Published: (2024)
Linear Strategic Classification with Endogenous Improvements
by: Shrivastava, Siddharth, et al.
Published: (2026)
by: Shrivastava, Siddharth, et al.
Published: (2026)
KCES: Training-Free Defense for Robust Graph Neural Networks via Kernel Complexity
by: Jia, Yaning, et al.
Published: (2025)
by: Jia, Yaning, et al.
Published: (2025)
CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging
by: Sun, Wenju, et al.
Published: (2025)
by: Sun, Wenju, et al.
Published: (2025)
Growing Efficient Accurate and Robust Neural Networks on the Edge
by: Sundaresha, Vignesh, et al.
Published: (2024)
by: Sundaresha, Vignesh, et al.
Published: (2024)
Adversarial Curriculum Graph-Free Knowledge Distillation for Graph Neural Networks
by: Jia, Yuang, et al.
Published: (2025)
by: Jia, Yuang, et al.
Published: (2025)
Gradient-Free Training of Quantized Neural Networks
by: Cohen, Noa, et al.
Published: (2024)
by: Cohen, Noa, et al.
Published: (2024)
Gradient Rewiring for Editable Graph Neural Network Training
by: Jiang, Zhimeng, et al.
Published: (2024)
by: Jiang, Zhimeng, et al.
Published: (2024)
Stealing Training Graphs from Graph Neural Networks
by: Lin, Minhua, et al.
Published: (2024)
by: Lin, Minhua, et al.
Published: (2024)
Conflict-Free Replicated Data Types for Neural Network Model Merging: A Two-Layer Architecture Enabling CRDT-Compliant Model Merging Across 26 Strategies
by: Gillespie, Ryan
Published: (2026)
by: Gillespie, Ryan
Published: (2026)
Karush-Kuhn-Tucker Condition-Trained Neural Networks (KKT Nets)
by: Arvind, Shreya, et al.
Published: (2024)
by: Arvind, Shreya, et al.
Published: (2024)
Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architectures
by: Khodabandeh, Ghazal, et al.
Published: (2025)
by: Khodabandeh, Ghazal, et al.
Published: (2025)
Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing
by: Fesser, Lukas, et al.
Published: (2025)
by: Fesser, Lukas, et al.
Published: (2025)
NAN: A Training-Free Solution to Coefficient Estimation in Model Merging
by: Si, Chongjie, et al.
Published: (2025)
by: Si, Chongjie, et al.
Published: (2025)
Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization
by: Liu, Ganlin, et al.
Published: (2024)
by: Liu, Ganlin, et al.
Published: (2024)
Training Robust Graph Neural Networks by Modeling Noise Dependencies
by: In, Yeonjun, et al.
Published: (2025)
by: In, Yeonjun, et al.
Published: (2025)
Demystifying Distributed Training of Graph Neural Networks for Link Prediction
by: Huang, Xin, et al.
Published: (2025)
by: Huang, Xin, et al.
Published: (2025)
Sparsity-Aware Communication for Distributed Graph Neural Network Training
by: Mukhodopadhyay, Ujjaini, et al.
Published: (2025)
by: Mukhodopadhyay, Ujjaini, et al.
Published: (2025)
Gradient-Free Training of Recurrent Neural Networks using Random Perturbations
by: Fernandez, Jesus Garcia, et al.
Published: (2024)
by: Fernandez, Jesus Garcia, et al.
Published: (2024)
Graph Neural Networks for Surfactant Multi-Property Prediction
by: Brozos, Christoforos, et al.
Published: (2024)
by: Brozos, Christoforos, et al.
Published: (2024)
Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation
by: Ge, Chendi, et al.
Published: (2025)
by: Ge, Chendi, et al.
Published: (2025)
Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training
by: Xia, Shuyin, et al.
Published: (2024)
by: Xia, Shuyin, et al.
Published: (2024)
Parameter-Free Structural-Diversity Message Passing for Graph Neural Networks
by: Kong, Mingyue, et al.
Published: (2025)
by: Kong, Mingyue, et al.
Published: (2025)
Quantile-Free Uncertainty Quantification in Graph Neural Networks
by: park, Soyoung, et al.
Published: (2026)
by: park, Soyoung, et al.
Published: (2026)
SeMe: Training-Free Language Model Merging via Semantic Alignment
by: Gu, Jian, et al.
Published: (2025)
by: Gu, Jian, et al.
Published: (2025)
SWAT-NN: Simultaneous Weights and Architecture Training for Neural Networks in a Latent Space
by: Huang, Zitong, et al.
Published: (2025)
by: Huang, Zitong, et al.
Published: (2025)
SGD as Free Energy Minimization: A Thermodynamic View on Neural Network Training
by: Sadrtdinov, Ildus, et al.
Published: (2025)
by: Sadrtdinov, Ildus, et al.
Published: (2025)
Scalable Back-Propagation-Free Training of Optical Physics-Informed Neural Networks
by: Zhao, Yequan, et al.
Published: (2025)
by: Zhao, Yequan, et al.
Published: (2025)
Towards Efficient Training of Graph Neural Networks: A Multiscale Approach
by: Gal, Eshed, et al.
Published: (2025)
by: Gal, Eshed, et al.
Published: (2025)
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks
by: Ge, Qingqing, et al.
Published: (2023)
by: Ge, Qingqing, et al.
Published: (2023)
Similar Items
-
EdgeMask-DG*: Learning Domain-Invariant Graph Structures via Adversarial Edge Masking
by: Bhattacharya, Rishabh, et al.
Published: (2026) -
Achieving Fair PCA Using Joint Eigenvalue Decomposition
by: Rathore, Vidhi, et al.
Published: (2025) -
DFORD: Directional Feedback based Online Ordinal Regression Learning
by: Manwani, Naresh, et al.
Published: (2025) -
Towards Calibrated Losses for Adversarial Robust Reject Option Classification
by: Shah, Vrund, et al.
Published: (2024) -
Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning
by: Gayen, Jayadratha, et al.
Published: (2025)