Copertina

xTime: Extreme Event Prediction with Hierarchical Knowledge Distillation and Expert Fusion

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Li, Quan, Yu, Wenchao, Wang, Suhang, Lin, Minhua, Chen, Lingwei, Cheng, Wei, Chen, Haifeng
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Hierarchical Knowledge Distillation on Text Graph for Data-limited Attribute Inference
di: Li, Quan, et al.
Pubblicazione: (2024)

Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models
di: Li, Quan, et al.
Pubblicazione: (2024)

TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents
di: Lee, Geon, et al.
Pubblicazione: (2025)

InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration
di: Wang, Fali, et al.
Pubblicazione: (2024)

LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning
di: Xu, Junjie, et al.
Pubblicazione: (2024)

PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep Intellectual Property Protection
di: Dai, Enyan, et al.
Pubblicazione: (2024)

TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop
di: Jiang, Yushan, et al.
Pubblicazione: (2025)

Interpretable Imitation Learning with Dynamic Causal Relations
di: Zhao, Tianxiang, et al.
Pubblicazione: (2023)

Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective
di: Zhang, Zhiwei, et al.
Pubblicazione: (2024)

Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection
di: Liu, Chen, et al.
Pubblicazione: (2024)

Consistently Informative Soft-Label Temperature for Knowledge Distillation
di: Luong, Hoang-Chau, et al.
Pubblicazione: (2026)

Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures
di: Li, Pingzhi, et al.
Pubblicazione: (2025)

Rethinking the Role of Temperature in Large Language Model Distillation
di: Luong, Hoang-Chau, et al.
Pubblicazione: (2026)

Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecasting
di: Shen, ChengAo, et al.
Pubblicazione: (2025)

Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting
di: Wang, Muyao, et al.
Pubblicazione: (2024)

Personalized Treatment Outcome Prediction from Scarce Data via Dual-Channel Knowledge Distillation and Adaptive Fusion
di: Chen, Wenjie, et al.
Pubblicazione: (2025)

Stealing Training Graphs from Graph Neural Networks
di: Lin, Minhua, et al.
Pubblicazione: (2024)

Robustness Inspired Graph Backdoor Defense
di: Zhang, Zhiwei, et al.
Pubblicazione: (2024)

DeRS: Towards Extremely Efficient Upcycled Mixture-of-Experts Models
di: Huang, Yongqi, et al.
Pubblicazione: (2025)

E4GEN: Event-level Explainable Extreme-Enhanced Time-series Generation
di: Jiang, Lin, et al.
Pubblicazione: (2026)

MST-Distill: Mixture of Specialized Teachers for Cross-Modal Knowledge Distillation
di: Li, Hui, et al.
Pubblicazione: (2025)

ScatterFusion: A Hierarchical Scattering Transform Framework for Enhanced Time Series Forecasting
di: Li, Wei
Pubblicazione: (2026)

Diversity-Aware Reverse Kullback-Leibler Divergence for Large Language Model Distillation
di: Luong, Hoang-Chau, et al.
Pubblicazione: (2026)

Graph Knowledge Distillation to Mixture of Experts
di: Rumiantsev, Pavel, et al.
Pubblicazione: (2024)

HyperFusion: Hierarchical Multimodal Ensemble Learning for Social Media Popularity Prediction
di: Ye, Liliang, et al.
Pubblicazione: (2025)

Taxon: Hierarchical Tax Code Prediction with Semantically Aligned LLM Expert Guidance
di: Li, Jihang, et al.
Pubblicazione: (2026)

Robust Knowledge Distillation Based on Feature Variance Against Backdoored Teacher Model
di: Chen, Jinyin, et al.
Pubblicazione: (2024)

Extreme Region Policy Distillation
di: Chen, Changyu, et al.
Pubblicazione: (2026)

Toward Robust Semi-supervised Regression via Dual-stream Knowledge Distillation
di: Su, Ye, et al.
Pubblicazione: (2025)

ReFine: Boosting Time Series Prediction of Extreme Events by Reweighting and Fine-tuning
di: Shi, Jimeng, et al.
Pubblicazione: (2024)

Capturing Extreme Events in Turbulence using an Extreme Variational Autoencoder (xVAE)
di: Zhang, Likun, et al.
Pubblicazione: (2025)

Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks
di: Lin, Minhua, et al.
Pubblicazione: (2024)

Federated Learning with Extremely Noisy Clients via Negative Distillation
di: Lu, Yang, et al.
Pubblicazione: (2023)

TimeExpert: Boosting Long Time Series Forecasting with Temporal Mix of Experts
di: Ma, Xiaowen, et al.
Pubblicazione: (2025)

SolverLLM: Leveraging Test-Time Scaling for Optimization Problem via LLM-Guided Search
di: Li, Dong, et al.
Pubblicazione: (2025)

Online Knowledge Distillation with Reward Guidance
di: Jia, Chen
Pubblicazione: (2025)

Spatial-Temporal Knowledge Distillation for Takeaway Recommendation
di: Zhao, Shuyuan, et al.
Pubblicazione: (2024)

POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning
di: Wang, Junxiang, et al.
Pubblicazione: (2023)

Tailored Federated Learning: Leveraging Direction Regulation & Knowledge Distillation
di: Tang, Huidong, et al.
Pubblicazione: (2024)

An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes
di: Chen, George H.
Pubblicazione: (2024)