:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Han, Tao, Shi, Huaixuan, Ding, Xinyi, Ma, Xiao, Gu, Huamao, Fang, Yili
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Machine Learning
Online Access:	https://arxiv.org/abs/2407.13268
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Mastering Massive Multi-Task Reinforcement Learning via Mixture-of-Expert Decision Transformer
by: Kong, Yilun, et al.
Published: (2025)

PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model
by: Liu, Yilun, et al.
Published: (2024)

HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Experts
by: Fang, Zihan, et al.
Published: (2026)

MTCNET: Multi-task Learning Paradigm for Crowd Count Estimation
by: Kumar, Abhay, et al.
Published: (2019)

MoDE: A Mixture-of-Experts Model with Mutual Distillation among the Experts
by: Xie, Zhitian, et al.
Published: (2024)

Supervised Mixture-of-Experts for Surgical Grasping and Retraction
by: Mazza, Lorenzo, et al.
Published: (2026)

SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning
by: Xie, Zhen-Hao, et al.
Published: (2026)

T-REX: Mixture-of-Rank-One-Experts with Semantic-aware Intuition for Multi-task Large Language Model Finetuning
by: Zhang, Rongyu, et al.
Published: (2024)

Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning
by: Zhao, Ziyu, et al.
Published: (2025)

Routing-Free Mixture-of-Experts
by: Liu, Yilun, et al.
Published: (2026)

Klotski: Efficient Mixture-of-Expert Inference via Expert-Aware Multi-Batch Pipeline
by: Fang, Zhiyuan, et al.
Published: (2025)

UniPool: A Globally Shared Expert Pool for Mixture-of-Experts
by: Huang, Minbin, et al.
Published: (2026)

Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity
by: Fang, Zihan, et al.
Published: (2026)

MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning
by: Wu, Han, et al.
Published: (2025)

Spatial-Temporal Mixture-of-Graph-Experts for Multi-Type Crime Prediction
by: Wu, Ziyang, et al.
Published: (2024)

Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs at Test Time
by: Han, Yixuan, et al.
Published: (2025)

Heterogeneous Graph Neural Networks with Loss-decrease-aware Curriculum Learning
by: Wang, Yili
Published: (2024)

Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis
by: Li, Weikai, et al.
Published: (2024)

MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts
by: Zhou, Jianan, et al.
Published: (2024)

Theory on Mixture-of-Experts in Continual Learning
by: Li, Hongbo, et al.
Published: (2024)

Multi-Head Mixture-of-Experts
by: Wu, Xun, et al.
Published: (2024)

Parameter-Efficient Routed Fine-Tuning: Mixture-of-Experts Demands Mixture of Adaptation Modules
by: Liu, Yilun, et al.
Published: (2025)

Shift Happens: Mixture of Experts based Continual Adaptation in Federated Learning
by: Bhope, Rahul Atul, et al.
Published: (2025)

Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning
by: Zhao, Ziyu, et al.
Published: (2024)

MMCTOP: A Multimodal Textualization and Mixture-of-Experts Framework for Clinical Trial Outcome Prediction
by: Aparício, Carolina, et al.
Published: (2025)

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts
by: Li, Cheng, et al.
Published: (2025)

Mixture-of-Experts Meets In-Context Reinforcement Learning
by: Wu, Wenhao, et al.
Published: (2025)

EnECG: Efficient Ensemble Learning for Electrocardiogram Multi-task Foundation Model
by: Xu, Yuhao, et al.
Published: (2025)

Mixture of Heterogeneous Grouped Experts for Language Modeling
by: Ma, Zhicheng, et al.
Published: (2026)

An Electrocardiogram Multi-task Benchmark with Comprehensive Evaluations and Insightful Findings
by: Xu, Yuhao, et al.
Published: (2025)

Mixture of Raytraced Experts
by: Perin, Andrea, et al.
Published: (2025)

Multi-Task Vehicle Routing Solver via Mixture of Specialized Experts under State-Decomposable MDP
by: Pan, Yuxin, et al.
Published: (2025)

LightMoE: Reducing Mixture-of-Experts Redundancy through Expert Replacing
by: Hao, Jiawei, et al.
Published: (2026)

CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community
by: Liu, Yan, et al.
Published: (2024)

Mixture of Experts in a Mixture of RL settings
by: Willi, Timon, et al.
Published: (2024)

mSFT: Addressing Dataset Mixtures Overfitting Heterogeneously in Multi-task SFT
by: Koh, Woosung, et al.
Published: (2026)

Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques
by: He, Shwai, et al.
Published: (2024)

Speculating Experts Accelerates Inference for Mixture-of-Experts
by: Madan, Vivan, et al.
Published: (2026)

Co-Supervised Learning: Improving Weak-to-Strong Generalization with Hierarchical Mixture of Experts
by: Liu, Yuejiang, et al.
Published: (2024)

Uncovering Intra-expert Activation Sparsity for Efficient Mixture-of-Expert Model Execution
by: Park, Jongseok, et al.
Published: (2026)