Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning
Fuente:
arXiv
Saved in:
| Main Authors: | Wei, Chu-Yuan, Liu, Shun-Yao, Zhuo, Sheng-Da, Wang, Chang-Dong, Huang, Shu-Qiang, Guizani, Mohsen |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
NeiGAD: Augmenting Graph Anomaly Detection via Spectral Neighbor Information
by: Qing, Qing, et al.
Published: (2026)
by: Qing, Qing, et al.
Published: (2026)
Toward Adaptive Large Language Models Structured Pruning via Hybrid-grained Weight Importance Assessment
by: Liu, Jun, et al.
Published: (2024)
by: Liu, Jun, et al.
Published: (2024)
Efficient Prompting for LLM-based Generative Internet of Things
by: Xiao, Bin, et al.
Published: (2024)
by: Xiao, Bin, et al.
Published: (2024)
Scalable Heterogeneous Graph Learning via Heterogeneous-aware Orthogonal Prototype Experts
by: Zhou, Wei, et al.
Published: (2026)
by: Zhou, Wei, et al.
Published: (2026)
Prompt-prompted Adaptive Structured Pruning for Efficient LLM Generation
by: Dong, Harry, et al.
Published: (2024)
by: Dong, Harry, et al.
Published: (2024)
GraphPro: Graph Pre-training and Prompt Learning for Recommendation
by: Yang, Yuhao, et al.
Published: (2023)
by: Yang, Yuhao, et al.
Published: (2023)
HGMP:Heterogeneous Graph Multi-Task Prompt Learning
by: Jiao, Pengfei, et al.
Published: (2025)
by: Jiao, Pengfei, et al.
Published: (2025)
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning
by: Guo, Zhuoning, et al.
Published: (2024)
by: Guo, Zhuoning, et al.
Published: (2024)
Trust Driven On-Demand Scheme for Client Deployment in Federated Learning
by: Chahoud, Mario, et al.
Published: (2024)
by: Chahoud, Mario, et al.
Published: (2024)
GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning
by: Lv, Rui, et al.
Published: (2025)
by: Lv, Rui, et al.
Published: (2025)
CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts
by: Li, Peiyuan, et al.
Published: (2026)
by: Li, Peiyuan, et al.
Published: (2026)
Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labels
by: Wang, Zhu, et al.
Published: (2024)
by: Wang, Zhu, et al.
Published: (2024)
Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables
by: Gong, Haisong, et al.
Published: (2024)
by: Gong, Haisong, et al.
Published: (2024)
PGB: One-Shot Pruning for BERT via Weight Grouping and Permutation
by: Lim, Hyemin, et al.
Published: (2025)
by: Lim, Hyemin, et al.
Published: (2025)
GCoT: Chain-of-Thought Prompt Learning for Graphs
by: Yu, Xingtong, et al.
Published: (2025)
by: Yu, Xingtong, et al.
Published: (2025)
Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective
by: Yao, Xinhao, et al.
Published: (2024)
by: Yao, Xinhao, et al.
Published: (2024)
LLM-assisted Semantic Option Discovery for Facilitating Adaptive Deep Reinforcement Learning
by: Yao, Chang, et al.
Published: (2026)
by: Yao, Chang, et al.
Published: (2026)
Adaptive Guidance for Local Training in Heterogeneous Federated Learning
by: Zhang, Jianqing, et al.
Published: (2024)
by: Zhang, Jianqing, et al.
Published: (2024)
EVA: Red-Teaming GUI Agents via Evolving Indirect Prompt Injection
by: Lu, Yijie, et al.
Published: (2025)
by: Lu, Yijie, et al.
Published: (2025)
IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models
by: Lee, Dong-Jae, et al.
Published: (2026)
by: Lee, Dong-Jae, et al.
Published: (2026)
Meta Pruning via Graph Metanetworks : A Universal Meta Learning Framework for Network Pruning
by: Liu, Yewei, et al.
Published: (2025)
by: Liu, Yewei, et al.
Published: (2025)
Multi-order Graph Clustering with Adaptive Node-level Weight Learning
by: Liu, Ye, et al.
Published: (2024)
by: Liu, Ye, et al.
Published: (2024)
PulmoFusion: Advancing Pulmonary Health with Efficient Multi-Modal Fusion
by: Sharshar, Ahmed, et al.
Published: (2025)
by: Sharshar, Ahmed, et al.
Published: (2025)
Large Multimodal Models for Embodied Intelligent Driving: The Next Frontier in Self-Driving?
by: Zhang, Long, et al.
Published: (2026)
by: Zhang, Long, et al.
Published: (2026)
Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning
by: Yang, Hanlin, et al.
Published: (2024)
by: Yang, Hanlin, et al.
Published: (2024)
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness
by: Zhu, Shengkun, et al.
Published: (2024)
by: Zhu, Shengkun, et al.
Published: (2024)
G-SAP: Graph-based Structure-Aware Prompt Learning over Heterogeneous Knowledge for Commonsense Reasoning
by: Dai, Ruiting, et al.
Published: (2024)
by: Dai, Ruiting, et al.
Published: (2024)
TPI-LLM: Serving 70B-scale LLMs Efficiently on Low-resource Edge Devices
by: Li, Zonghang, et al.
Published: (2024)
by: Li, Zonghang, et al.
Published: (2024)
LLM-Based SQL Generation: Prompting, Self-Refinement, and Adaptive Weighted Majority Voting
by: Yang, Yu-Jie, et al.
Published: (2026)
by: Yang, Yu-Jie, et al.
Published: (2026)
SwiftPrune: Hessian-Free Weight Pruning for Large Language Models
by: Kang, Yuhan, et al.
Published: (2025)
by: Kang, Yuhan, et al.
Published: (2025)
RAP: Runtime Adaptive Pruning for LLM Inference
by: Liu, Huanrong, et al.
Published: (2025)
by: Liu, Huanrong, et al.
Published: (2025)
Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning
by: Hu, Yulan, et al.
Published: (2023)
by: Hu, Yulan, et al.
Published: (2023)
Beyond One-Way Pruning: Bidirectional Pruning-Regrowth for Extreme Accuracy-Sparsity Tradeoff
by: Liu, Junchen, et al.
Published: (2025)
by: Liu, Junchen, et al.
Published: (2025)
Vision-Language Models for Edge Networks: A Comprehensive Survey
by: Sharshar, Ahmed, et al.
Published: (2025)
by: Sharshar, Ahmed, et al.
Published: (2025)
ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron Pruning
by: Chavhan, Ruchika, et al.
Published: (2024)
by: Chavhan, Ruchika, et al.
Published: (2024)
Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection
by: Ling, Zhiwei, et al.
Published: (2026)
by: Ling, Zhiwei, et al.
Published: (2026)
Prima.cpp: Fast 30-70B LLM Inference on Heterogeneous and Low-Resource Home Clusters
by: Li, Zonghang, et al.
Published: (2025)
by: Li, Zonghang, et al.
Published: (2025)
Structure-Aware Automatic Channel Pruning by Searching with Graph Embedding
by: Liu, Zifan, et al.
Published: (2025)
by: Liu, Zifan, et al.
Published: (2025)
Prompt-Driven Continual Graph Learning
by: Wang, Qi, et al.
Published: (2025)
by: Wang, Qi, et al.
Published: (2025)
PromptHash: Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing Retrieval
by: Zou, Qiang, et al.
Published: (2025)
by: Zou, Qiang, et al.
Published: (2025)
Similar Items
-
NeiGAD: Augmenting Graph Anomaly Detection via Spectral Neighbor Information
by: Qing, Qing, et al.
Published: (2026) -
Toward Adaptive Large Language Models Structured Pruning via Hybrid-grained Weight Importance Assessment
by: Liu, Jun, et al.
Published: (2024) -
Efficient Prompting for LLM-based Generative Internet of Things
by: Xiao, Bin, et al.
Published: (2024) -
Scalable Heterogeneous Graph Learning via Heterogeneous-aware Orthogonal Prototype Experts
by: Zhou, Wei, et al.
Published: (2026) -
Prompt-prompted Adaptive Structured Pruning for Efficient LLM Generation
by: Dong, Harry, et al.
Published: (2024)