Towards a deeper GCN: Alleviate over-smoothing with iterative training and fine-tuning
Fuente:
arXiv
Saved in:
| Main Authors: | Peng, Furong, Gao, Jinzhen, Lu, Xuan, Liu, Kang, Huo, Yifan, Wang, Sheng |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
TSC: A Simple Two-Sided Constraint against Over-Smoothing
by: Peng, Furong, et al.
Published: (2024)
by: Peng, Furong, et al.
Published: (2024)
Replaying pre-training data improves fine-tuning
by: Kotha, Suhas, et al.
Published: (2026)
by: Kotha, Suhas, et al.
Published: (2026)
Improving embedding with contrastive fine-tuning on small datasets with expert-augmented scores
by: Lu, Jun, et al.
Published: (2024)
by: Lu, Jun, et al.
Published: (2024)
Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem
by: Huang, Chen, et al.
Published: (2024)
by: Huang, Chen, et al.
Published: (2024)
MLC-GCN: Multi-Level Generated Connectome Based GCN for AD Analysis
by: Zhu, Wenqi, et al.
Published: (2024)
by: Zhu, Wenqi, et al.
Published: (2024)
Complexity-aware fine-tuning
by: Goncharov, Andrey, et al.
Published: (2025)
by: Goncharov, Andrey, et al.
Published: (2025)
Universal pre-training by iterated random computation
by: Bloem, Peter
Published: (2025)
by: Bloem, Peter
Published: (2025)
MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators
by: Wan, Cheng, et al.
Published: (2025)
by: Wan, Cheng, et al.
Published: (2025)
Quantum-PEFT: Ultra parameter-efficient fine-tuning
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
RegionGCN: Spatial-Heterogeneity-Aware Graph Convolutional Networks
by: Guo, Hao, et al.
Published: (2025)
by: Guo, Hao, et al.
Published: (2025)
A deeper look at depth pruning of LLMs
by: Siddiqui, Shoaib Ahmed, et al.
Published: (2024)
by: Siddiqui, Shoaib Ahmed, et al.
Published: (2024)
Learning from models beyond fine-tuning
by: Zheng, Hongling, et al.
Published: (2023)
by: Zheng, Hongling, et al.
Published: (2023)
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks
by: Jain, Samyak, et al.
Published: (2023)
by: Jain, Samyak, et al.
Published: (2023)
Layer-wise LoRA fine-tuning: a similarity metric approach
by: Ogawa, Keith Ando, et al.
Published: (2026)
by: Ogawa, Keith Ando, et al.
Published: (2026)
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning
by: Barceló, Roberto, et al.
Published: (2024)
by: Barceló, Roberto, et al.
Published: (2024)
ReALLM: A general framework for LLM compression and fine-tuning
by: Leconte, Louis, et al.
Published: (2024)
by: Leconte, Louis, et al.
Published: (2024)
Beyond Accuracy: On the Effects of Fine-tuning Towards Vision-Language Model's Prediction Rationality
by: Wang, Qitong, et al.
Published: (2024)
by: Wang, Qitong, et al.
Published: (2024)
Empirical influence functions to understand the logic of fine-tuning
by: Matelsky, Jordan K., et al.
Published: (2024)
by: Matelsky, Jordan K., et al.
Published: (2024)
On fine-tuning Boltz-2 for protein-protein affinity prediction
by: King, James, et al.
Published: (2025)
by: King, James, et al.
Published: (2025)
Delayed Bottlenecking: Alleviating Forgetting in Pre-trained Graph Neural Networks
by: Zhao, Zhe, et al.
Published: (2024)
by: Zhao, Zhe, et al.
Published: (2024)
Towards Compatible Fine-tuning for Vision-Language Model Updates
by: Wang, Zhengbo, et al.
Published: (2024)
by: Wang, Zhengbo, et al.
Published: (2024)
RI-Loss: A Learnable Residual-Informed Loss for Time Series Forecasting
by: Wang, Jieting, et al.
Published: (2025)
by: Wang, Jieting, et al.
Published: (2025)
Stepsize anything: A unified learning rate schedule for budgeted-iteration training
by: Tang, Anda, et al.
Published: (2025)
by: Tang, Anda, et al.
Published: (2025)
PromptGCN: Bridging Subgraph Gaps in Lightweight GCNs
by: Ji, Shengwei, et al.
Published: (2024)
by: Ji, Shengwei, et al.
Published: (2024)
Adaptive parameter-efficient fine-tuning via Hessian-informed subset selection
by: Xu, Shiyun, et al.
Published: (2025)
by: Xu, Shiyun, et al.
Published: (2025)
Domain Adaptation for Time series Transformers using One-step fine-tuning
by: Khanal, Subina, et al.
Published: (2024)
by: Khanal, Subina, et al.
Published: (2024)
The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models
by: Alabdallah, Abdallah, et al.
Published: (2022)
by: Alabdallah, Abdallah, et al.
Published: (2022)
Towards Robust Multimodal Learning in the Open World
by: Huo, Fushuo
Published: (2025)
by: Huo, Fushuo
Published: (2025)
Watch the Weights: Unsupervised monitoring and control of fine-tuned LLMs
by: Zhong, Ziqian, et al.
Published: (2025)
by: Zhong, Ziqian, et al.
Published: (2025)
Gradient dynamics for low-rank fine-tuning beyond kernels
by: Dayi, Arif Kerem, et al.
Published: (2024)
by: Dayi, Arif Kerem, et al.
Published: (2024)
LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs
by: Jha, Piyush, et al.
Published: (2024)
by: Jha, Piyush, et al.
Published: (2024)
SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-training
by: Zhang, Gengwei, et al.
Published: (2024)
by: Zhang, Gengwei, et al.
Published: (2024)
Relational Knowledge Distillation Using Fine-tuned Function Vectors
by: Kang, Andrea, et al.
Published: (2026)
by: Kang, Andrea, et al.
Published: (2026)
MLP Fusion: Towards Efficient Fine-tuning of Dense and Mixture-of-Experts Language Models
by: Ai, Mengting, et al.
Published: (2023)
by: Ai, Mengting, et al.
Published: (2023)
Selective Pre-training for Private Fine-tuning
by: Yu, Da, et al.
Published: (2023)
by: Yu, Da, et al.
Published: (2023)
GCL-GCN: Graphormer and Contrastive Learning Enhanced Attributed Graph Clustering Network
by: Li, Binxiong, et al.
Published: (2025)
by: Li, Binxiong, et al.
Published: (2025)
Alleviating Over-Smoothing via Aggregation over Compact Manifolds
by: Zhou, Dongzhuoran, et al.
Published: (2024)
by: Zhou, Dongzhuoran, et al.
Published: (2024)
Leveraging Duration Pseudo-Embeddings in Multilevel LSTM and GCN Hypermodels for Outcome-Oriented PPM
by: Wang, Fang, et al.
Published: (2025)
by: Wang, Fang, et al.
Published: (2025)
SStaGCN: Simplified stacking based graph convolutional networks
by: Cai, Jia, et al.
Published: (2021)
by: Cai, Jia, et al.
Published: (2021)
Learning to Price Bundles: A GCN Approach for Mixed Bundling
by: Ding, Liangyu, et al.
Published: (2025)
by: Ding, Liangyu, et al.
Published: (2025)
Similar Items
-
TSC: A Simple Two-Sided Constraint against Over-Smoothing
by: Peng, Furong, et al.
Published: (2024) -
Replaying pre-training data improves fine-tuning
by: Kotha, Suhas, et al.
Published: (2026) -
Improving embedding with contrastive fine-tuning on small datasets with expert-augmented scores
by: Lu, Jun, et al.
Published: (2024) -
Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem
by: Huang, Chen, et al.
Published: (2024) -
MLC-GCN: Multi-Level Generated Connectome Based GCN for AD Analysis
by: Zhu, Wenqi, et al.
Published: (2024)