Copertina

Orchestrating Heterogeneous Experts: A Scalable MoE Framework with Anisotropy-Preserving Fusion

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Liu, Ye, Chen, Xu, Chen, Wuji, Li, Mang
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Information Retrieval Artificial Intelligence Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Hierarchical LoRA MoE for Efficient CTR Model Scaling
di: Zeng, Zhichen, et al.
Pubblicazione: (2025)

Enhancing Talent Search Ranking with Role-Aware Expert Mixtures and LLM-based Fine-Grained Job Descriptions
di: Li, Jihang, et al.
Pubblicazione: (2025)

Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training
di: Wang, Mengru, et al.
Pubblicazione: (2025)

Not All Candidates are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems
di: Tong, Pengfei, et al.
Pubblicazione: (2026)

Enhancing Performance and Scalability of Large-Scale Recommendation Systems with Jagged Flash Attention
di: Xu, Rengan, et al.
Pubblicazione: (2024)

InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
di: Zeng, Zhichen, et al.
Pubblicazione: (2024)

MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization
di: Yaggel, Ken, et al.
Pubblicazione: (2025)

Save, Revisit, Retain: A Scalable Framework for Enhancing User Retention in Large-Scale Recommender Systems
di: Jiang, Weijie, et al.
Pubblicazione: (2025)

WarpAdam: A new Adam optimizer based on Meta-Learning approach
di: Pan, Chengxi, et al.
Pubblicazione: (2024)

Movie Recommendation with Poster Attention via Multi-modal Transformer Feature Fusion
di: Xia, Linhan, et al.
Pubblicazione: (2024)

Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning
di: Han, Xiao, et al.
Pubblicazione: (2024)

Federated Vision-Language-Recommendation with Personalized Fusion
di: Li, Zhiwei, et al.
Pubblicazione: (2024)

DiffGraph: Heterogeneous Graph Diffusion Model
di: Li, Zongwei, et al.
Pubblicazione: (2025)

MBD: A Model-Based Debiasing Framework Across User, Content, and Model Dimensions
di: Li, Yuantong, et al.
Pubblicazione: (2026)

Ripple Knowledge Graph Convolutional Networks For Recommendation Systems
di: Li, Chen, et al.
Pubblicazione: (2023)

Order-Preserving Dimension Reduction for Multimodal Semantic Embedding
di: Gong, Chengyu, et al.
Pubblicazione: (2024)

Privacy Preserving Inference of Personalized Content for Out of Matrix Users
di: Sun, Michael, et al.
Pubblicazione: (2025)

Scalable Neural Network Training over Distributed Graphs
di: Kolluri, Aashish, et al.
Pubblicazione: (2023)

RAG4Outcome: A Retrieval-Augmented Multimodal Framework for Prognostic Prediction in Chronic Osteomyelitis
di: Shi, Daqian, et al.
Pubblicazione: (2026)

Heuristic Learning with Graph Neural Networks: A Unified Framework for Link Prediction
di: Zhang, Juzheng, et al.
Pubblicazione: (2024)

Lookahead: An Inference Acceleration Framework for Large Language Model with Lossless Generation Accuracy
di: Zhao, Yao, et al.
Pubblicazione: (2023)

Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank
di: Chen, Mouxiang, et al.
Pubblicazione: (2023)

Enhancing Recommendation with Denoising Auxiliary Task
di: Liu, Pengsheng, et al.
Pubblicazione: (2024)

Contextual Attention-Based Multimodal Fusion of LLM and CNN for Sentiment Analysis
di: Zerkouk, Meriem, et al.
Pubblicazione: (2025)

ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation
di: Hou, Chaoqun, et al.
Pubblicazione: (2024)

Modeling Behavioral Intensity and Transitions for Generative Recommendation
di: Yang, Wenxuan, et al.
Pubblicazione: (2026)

XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation
di: Li, Zhuoling, et al.
Pubblicazione: (2026)

Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution
di: Zhang, Shengjia, et al.
Pubblicazione: (2025)

VALUE: Value-Aware Large Language Model for Query Rewriting via Weighted Trie in Sponsored Search
di: Zhang, Xiao, et al.
Pubblicazione: (2025)

BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations
di: Mishra, Ashirbad, et al.
Pubblicazione: (2025)

BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models
di: Yang, Weiqin, et al.
Pubblicazione: (2026)

NextMem: Towards Latent Factual Memory for LLM-based Agents
di: Zhang, Zeyu, et al.
Pubblicazione: (2026)

Recall: Empowering Multimodal Embedding for Edge Devices
di: Cai, Dongqi, et al.
Pubblicazione: (2024)

Learn to Memorize: Optimizing LLM-based Agents with Adaptive Memory Framework
di: Zhang, Zeyu, et al.
Pubblicazione: (2025)

DKINet: Medication Recommendation via Domain Knowledge Informed Deep Learning
di: Liu, Sicen, et al.
Pubblicazione: (2023)

Knowledge-Enhanced Recommendation with User-Centric Subgraph Network
di: Liu, Guangyi, et al.
Pubblicazione: (2024)

LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation
di: Jiang, Shali, et al.
Pubblicazione: (2026)

PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System
di: Wang, Jialiang, et al.
Pubblicazione: (2024)

GReF: A Unified Generative Framework for Efficient Reranking via Ordered Multi-token Prediction
di: Lin, Zhijie, et al.
Pubblicazione: (2025)

AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMs
di: Wang, Yubo, et al.
Pubblicazione: (2025)