ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
Fuente:
arXiv
Saved in:
| Main Authors: | Xu, Yuhao, Wang, Xiaoda, Wu, Yi, Jin, Wei, Hu, Xiao, Yang, Carl |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
EnECG: Efficient Ensemble Learning for Electrocardiogram Multi-task Foundation Model
by: Xu, Yuhao, et al.
Published: (2025)
by: Xu, Yuhao, et al.
Published: (2025)
Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation
by: Wang, Xiaoda, et al.
Published: (2025)
by: Wang, Xiaoda, et al.
Published: (2025)
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
by: Shi, Xiaoming, et al.
Published: (2024)
by: Shi, Xiaoming, et al.
Published: (2024)
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024)
by: Jin, Peng, et al.
Published: (2024)
MoE-Loco: Mixture of Experts for Multitask Locomotion
by: Huang, Runhan, et al.
Published: (2025)
by: Huang, Runhan, et al.
Published: (2025)
DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts
by: Feng, Jiarui, et al.
Published: (2026)
by: Feng, Jiarui, et al.
Published: (2026)
EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models
by: Chen, Yuanteng, et al.
Published: (2025)
by: Chen, Yuanteng, et al.
Published: (2025)
PWC-MoE: Privacy-Aware Wireless Collaborative Mixture of Experts
by: Su, Yang, et al.
Published: (2025)
by: Su, Yang, et al.
Published: (2025)
Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts
by: Gu, Naibin, et al.
Published: (2025)
by: Gu, Naibin, et al.
Published: (2025)
MoE-Health: A Mixture of Experts Framework for Robust Multimodal Healthcare Prediction
by: Wang, Xiaoyang, et al.
Published: (2025)
by: Wang, Xiaoyang, 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)
Mixture of Experts (MoE): A Big Data Perspective
by: Gan, Wensheng, et al.
Published: (2025)
by: Gan, Wensheng, et al.
Published: (2025)
Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models
by: Wei, Tianwen, et al.
Published: (2024)
by: Wei, Tianwen, et al.
Published: (2024)
MoE-I$^2$: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition
by: Yang, Cheng, et al.
Published: (2024)
by: Yang, Cheng, et al.
Published: (2024)
Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts
by: Yun, Sukwon, et al.
Published: (2024)
by: Yun, Sukwon, et al.
Published: (2024)
Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference
by: Liu, Baihui, et al.
Published: (2026)
by: Liu, Baihui, et al.
Published: (2026)
Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling
by: Jiang, Fan, et al.
Published: (2026)
by: Jiang, Fan, et al.
Published: (2026)
DA-MoE: Addressing Depth-Sensitivity in Graph-Level Analysis through Mixture of Experts
by: Yao, Zelin, et al.
Published: (2024)
by: Yao, Zelin, et al.
Published: (2024)
FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge
by: Hu, Gang, et al.
Published: (2025)
by: Hu, Gang, et al.
Published: (2025)
TolerantECG: A Foundation Model for Imperfect Electrocardiogram
by: Nguyen, Huynh Dang, et al.
Published: (2025)
by: Nguyen, Huynh Dang, et al.
Published: (2025)
Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts
by: Li, Yunxin, et al.
Published: (2024)
by: Li, Yunxin, et al.
Published: (2024)
Symphony-MoE: Harmonizing Disparate Pre-trained Models into a Coherent Mixture-of-Experts
by: Wang, Qi, et al.
Published: (2025)
by: Wang, Qi, et al.
Published: (2025)
An Electrocardiogram Multi-task Benchmark with Comprehensive Evaluations and Insightful Findings
by: Xu, Yuhao, et al.
Published: (2025)
by: Xu, Yuhao, et al.
Published: (2025)
MoE-DisCo:Low Economy Cost Training Mixture-of-Experts Models
by: Ye, Xin, et al.
Published: (2026)
by: Ye, Xin, et al.
Published: (2026)
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts
by: Pióro, Maciej, et al.
Published: (2024)
by: Pióro, Maciej, et al.
Published: (2024)
TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts
by: Xu, Yu, et al.
Published: (2026)
by: Xu, Yu, et al.
Published: (2026)
DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training
by: Jin, Can, et al.
Published: (2025)
by: Jin, Can, et al.
Published: (2025)
Input Domain Aware MoE: Decoupling Routing Decisions from Task Optimization in Mixture of Experts
by: Hua, Yongxiang, et al.
Published: (2025)
by: Hua, Yongxiang, et al.
Published: (2025)
$μ$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
MultiPL-MoE: Multi-Programming-Lingual Extension of Large Language Models through Hybrid Mixture-of-Experts
by: Wang, Qing, et al.
Published: (2025)
by: Wang, Qing, et al.
Published: (2025)
MP-MoE: Matrix Profile-Guided Mixture of Experts for Precipitation Forecasting
by: Tran, Huyen Ngoc, et al.
Published: (2026)
by: Tran, Huyen Ngoc, et al.
Published: (2026)
Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection
by: Lei, Tianwu, et al.
Published: (2024)
by: Lei, Tianwu, et al.
Published: (2024)
CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
by: Liu, Yang, et al.
Published: (2026)
by: Liu, Yang, et al.
Published: (2026)
Linear-MoE: Linear Sequence Modeling Meets Mixture-of-Experts
by: Sun, Weigao, et al.
Published: (2025)
by: Sun, Weigao, et al.
Published: (2025)
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference
by: Hwang, Ranggi, et al.
Published: (2023)
by: Hwang, Ranggi, et al.
Published: (2023)
Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient
by: Ludziejewski, Jan, et al.
Published: (2025)
by: Ludziejewski, Jan, et al.
Published: (2025)
MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router
by: Xie, Yanyue, et al.
Published: (2024)
by: Xie, Yanyue, et al.
Published: (2024)
KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
by: Xu, Zukang, et al.
Published: (2026)
by: Xu, Zukang, et al.
Published: (2026)
Expert Divergence Learning for MoE-based Language Models
by: Li, Jiaang, et al.
Published: (2026)
by: Li, Jiaang, et al.
Published: (2026)
OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale
by: Shi, Jingze, et al.
Published: (2026)
by: Shi, Jingze, et al.
Published: (2026)
Similar Items
-
EnECG: Efficient Ensemble Learning for Electrocardiogram Multi-task Foundation Model
by: Xu, Yuhao, et al.
Published: (2025) -
Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation
by: Wang, Xiaoda, et al.
Published: (2025) -
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
by: Shi, Xiaoming, et al.
Published: (2024) -
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024) -
MoE-Loco: Mixture of Experts for Multitask Locomotion
by: Huang, Runhan, et al.
Published: (2025)