Graph Knowledge Distillation to Mixture of Experts
Fuente:
arXiv
Saved in:
| Main Authors: | Rumiantsev, Pavel, Coates, Mark |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Sparse Decomposition of Graph Neural Networks
by: Hu, Yaochen, et al.
Published: (2024)
by: Hu, Yaochen, et al.
Published: (2024)
Variation Matters: from Mitigating to Embracing Zero-Shot NAS Ranking Function Variation
by: Rumiantsev, Pavel, et al.
Published: (2025)
by: Rumiantsev, Pavel, et al.
Published: (2025)
Half Search Space is All You Need
by: Rumiantsev, Pavel, et al.
Published: (2025)
by: Rumiantsev, Pavel, et al.
Published: (2025)
GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection
by: Zhang, Zhanguang, et al.
Published: (2024)
by: Zhang, Zhanguang, et al.
Published: (2024)
Every Expert Matters: Towards Effective Knowledge Distillation for Mixture-of-Experts Language Models
by: Kim, Gyeongman, et al.
Published: (2025)
by: Kim, Gyeongman, et al.
Published: (2025)
MoDE: A Mixture-of-Experts Model with Mutual Distillation among the Experts
by: Xie, Zhitian, et al.
Published: (2024)
by: Xie, Zhitian, et al.
Published: (2024)
Variational Distillation of Diffusion Policies into Mixture of Experts
by: Zhou, Hongyi, et al.
Published: (2024)
by: Zhou, Hongyi, et al.
Published: (2024)
Mixture of Weak & Strong Experts on Graphs
by: Zeng, Hanqing, et al.
Published: (2023)
by: Zeng, Hanqing, et al.
Published: (2023)
Modeling Expert Interactions in Sparse Mixture of Experts via Graph Structures
by: Nguyen-Nhat, Minh-Khoi, et al.
Published: (2025)
by: Nguyen-Nhat, Minh-Khoi, et al.
Published: (2025)
Pruning and Distilling Mixture-of-Experts into Dense Language Models
by: Kim, Junhyuck, et al.
Published: (2026)
by: Kim, Junhyuck, et al.
Published: (2026)
Graph-Conditioned Mixture of Graph Neural Network Experts for Traffic Forecasting
by: Ghaffari, Amirhossein, et al.
Published: (2026)
by: Ghaffari, Amirhossein, et al.
Published: (2026)
FEval-TTC: Fair Evaluation Protocol for Test-Time Compute
by: Rumiantsev, Pavel, et al.
Published: (2025)
by: Rumiantsev, Pavel, et al.
Published: (2025)
SDG-MoE: Signed Debate Graph Mixture-of-Experts
by: Kulibaba, Stepan, et al.
Published: (2026)
by: Kulibaba, Stepan, et al.
Published: (2026)
Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environments
by: Liu, Junming, et al.
Published: (2025)
by: Liu, Junming, et al.
Published: (2025)
Mixture of Raytraced Experts
by: Perin, Andrea, et al.
Published: (2025)
by: Perin, Andrea, et al.
Published: (2025)
Spatial-Temporal Mixture-of-Graph-Experts for Multi-Type Crime Prediction
by: Wu, Ziyang, et al.
Published: (2024)
by: Wu, Ziyang, et al.
Published: (2024)
Zero-shot Generalizable Graph Anomaly Detection with Mixture of Riemannian Experts
by: Zhao, Xinyu, et al.
Published: (2026)
by: Zhao, Xinyu, et al.
Published: (2026)
Mixture of Experts in a Mixture of RL settings
by: Willi, Timon, et al.
Published: (2024)
by: Willi, Timon, et al.
Published: (2024)
Speculating Experts Accelerates Inference for Mixture-of-Experts
by: Madan, Vivan, et al.
Published: (2026)
by: Madan, Vivan, et al.
Published: (2026)
GraphMoRE: Mitigating Topological Heterogeneity via Mixture of Riemannian Experts
by: Guo, Zihao, et al.
Published: (2024)
by: Guo, Zihao, et al.
Published: (2024)
Geometric Mixture-of-Experts with Curvature-Guided Adaptive Routing for Graph Representation Learning
by: Cao, Haifang, et al.
Published: (2026)
by: Cao, Haifang, et al.
Published: (2026)
MoSE: Unveiling Structural Patterns in Graphs via Mixture of Subgraph Experts
by: Ye, Junda, et al.
Published: (2025)
by: Ye, Junda, et al.
Published: (2025)
MC#: Mixture Compressor for Mixture-of-Experts Large Models
by: Huang, Wei, et al.
Published: (2025)
by: Huang, Wei, et al.
Published: (2025)
Low-Dimensional Federated Knowledge Graph Embedding via Knowledge Distillation
by: Zhang, Xiaoxiong, et al.
Published: (2024)
by: Zhang, Xiaoxiong, et al.
Published: (2024)
Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures
by: Li, Pingzhi, et al.
Published: (2025)
by: Li, Pingzhi, et al.
Published: (2025)
Mixture of Diverse Size Experts
by: Sun, Manxi, et al.
Published: (2024)
by: Sun, Manxi, et al.
Published: (2024)
Mixture of A Million Experts
by: He, Xu Owen
Published: (2024)
by: He, Xu Owen
Published: (2024)
Efficiently Editing Mixture-of-Experts Models with Compressed Experts
by: He, Yifei, et al.
Published: (2025)
by: He, Yifei, et al.
Published: (2025)
Sparsity and Superposition in Mixture of Experts
by: Chaudhari, Marmik, et al.
Published: (2025)
by: Chaudhari, Marmik, et al.
Published: (2025)
Mixture of Concept Bottleneck Experts
by: De Santis, Francesco, et al.
Published: (2026)
by: De Santis, Francesco, et al.
Published: (2026)
CKGConv: General Graph Convolution with Continuous Kernels
by: Ma, Liheng, et al.
Published: (2024)
by: Ma, Liheng, et al.
Published: (2024)
Multi-Teacher Knowledge Distillation via Teacher-Informed Mixture Priors
by: Fang, Luyang, et al.
Published: (2026)
by: Fang, Luyang, et al.
Published: (2026)
Online Adversarial Knowledge Distillation for Graph Neural Networks
by: Wang, Can, et al.
Published: (2021)
by: Wang, Can, et al.
Published: (2021)
AnyExperts: On-Demand Expert Allocation for Multimodal Language Models with Mixture of Expert
by: Gao, Yuting, et al.
Published: (2025)
by: Gao, Yuting, et al.
Published: (2025)
Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection
by: Huang, Xiaoyu, et al.
Published: (2024)
by: Huang, Xiaoyu, et al.
Published: (2024)
Accelerating Mixture-of-Expert Inference with Adaptive Expert Split Mechanism
by: Yan, Jiaming, et al.
Published: (2025)
by: Yan, Jiaming, et al.
Published: (2025)
Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts
by: Dwivedi, Chaitanya, et al.
Published: (2026)
by: Dwivedi, Chaitanya, et al.
Published: (2026)
C3PO: Optimized Large Language Model Cascades with Probabilistic Cost Constraints for Reasoning
by: Valkanas, Antonios, et al.
Published: (2025)
by: Valkanas, Antonios, et al.
Published: (2025)
Theory on Mixture-of-Experts in Continual Learning
by: Li, Hongbo, et al.
Published: (2024)
by: Li, Hongbo, et al.
Published: (2024)
Mixture of Experts in Large Language Models
by: Zhang, Danyang, et al.
Published: (2025)
by: Zhang, Danyang, et al.
Published: (2025)
Similar Items
-
Sparse Decomposition of Graph Neural Networks
by: Hu, Yaochen, et al.
Published: (2024) -
Variation Matters: from Mitigating to Embracing Zero-Shot NAS Ranking Function Variation
by: Rumiantsev, Pavel, et al.
Published: (2025) -
Half Search Space is All You Need
by: Rumiantsev, Pavel, et al.
Published: (2025) -
GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection
by: Zhang, Zhanguang, et al.
Published: (2024) -
Every Expert Matters: Towards Effective Knowledge Distillation for Mixture-of-Experts Language Models
by: Kim, Gyeongman, et al.
Published: (2025)