Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Xu, Deng, Junwei, Xu, Chang, Li, Hao, Bian, Jiang |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts
by: Huang, Yu-Hao, et al.
Published: (2025)
by: Huang, Yu-Hao, 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)
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process
by: Fan, Xinyao, et al.
Published: (2024)
by: Fan, Xinyao, 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)
Latent Laplace Diffusion for Irregular Multivariate Time Series
by: You, Zinuo, et al.
Published: (2026)
by: You, Zinuo, et al.
Published: (2026)
Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition
by: Li, Dongyuan, et al.
Published: (2026)
by: Li, Dongyuan, et al.
Published: (2026)
Continuous-Time Linear Positional Embedding for Irregular Time Series Forecasting
by: Kim, Byunghyun, et al.
Published: (2024)
by: Kim, Byunghyun, et al.
Published: (2024)
ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
by: Chen, Yuqi, et al.
Published: (2024)
by: Chen, Yuqi, et al.
Published: (2024)
MoE-PHDS: One MoE checkpoint for flexible runtime sparsity
by: Hannah, Lauren. A, et al.
Published: (2025)
by: Hannah, Lauren. A, et al.
Published: (2025)
Ban&Pick: Ehancing Performance and Efficiency of MoE-LLMs via Smarter Routing
by: Chen, Yuanteng, et al.
Published: (2025)
by: Chen, Yuanteng, et al.
Published: (2025)
GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory
by: Wu, Haoze, et al.
Published: (2024)
by: Wu, Haoze, et al.
Published: (2024)
A Statistical Approach for Modeling Irregular Multivariate Time Series with Missing Observations
by: Nie, Dingyi, et al.
Published: (2026)
by: Nie, Dingyi, et al.
Published: (2026)
Seg-MoE: Multi-Resolution Segment-wise Mixture-of-Experts for Time Series Forecasting Transformers
by: Ortigossa, Evandro S., et al.
Published: (2026)
by: Ortigossa, Evandro S., et al.
Published: (2026)
Innovator: Scientific Continued Pretraining with Fine-grained MoE Upcycling
by: Liao, Ning, et al.
Published: (2025)
by: Liao, Ning, et al.
Published: (2025)
Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models
by: Hou, Xuan, et al.
Published: (2025)
by: Hou, Xuan, et al.
Published: (2025)
TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation
by: Deng, Bowen, et al.
Published: (2025)
by: Deng, Bowen, et al.
Published: (2025)
GDFlow: Anomaly Detection with NCDE-based Normalizing Flow for Advanced Driver Assistance System
by: Lee, Kangjun, et al.
Published: (2024)
by: Lee, Kangjun, et al.
Published: (2024)
Grouter: Decoupling Routing from Representation for Accelerated MoE Training
by: Xu, Yuqi, et al.
Published: (2026)
by: Xu, Yuqi, et al.
Published: (2026)
PWC-MoE: Privacy-Aware Wireless Collaborative Mixture of Experts
by: Su, Yang, et al.
Published: (2025)
by: Su, Yang, et al.
Published: (2025)
XTSFormer: Cross-Temporal-Scale Transformer for Irregular-Time Event Prediction in Clinical Applications
by: Xiao, Tingsong, et al.
Published: (2024)
by: Xiao, Tingsong, et al.
Published: (2024)
GRIN: GRadient-INformed MoE
by: Liu, Liyuan, et al.
Published: (2024)
by: Liu, Liyuan, et al.
Published: (2024)
Unleashing The Power of Pre-Trained Language Models for Irregularly Sampled Time Series
by: Zhang, Weijia, et al.
Published: (2024)
by: Zhang, Weijia, et al.
Published: (2024)
A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning
by: Xu, Junzhou, et al.
Published: (2025)
by: Xu, Junzhou, et al.
Published: (2025)
MoETTA: Test-Time Adaptation Under Mixed Distribution Shifts with MoE-LayerNorm
by: Fan, Xiao, et al.
Published: (2025)
by: Fan, Xiao, et al.
Published: (2025)
DOT-MoE: Differentiable Optimal Transport for MoEfication
by: Bamba, Udbhav, et al.
Published: (2026)
by: Bamba, Udbhav, et al.
Published: (2026)
Collaborative Compression for Large-Scale MoE Deployment on Edge
by: Chen, Yixiao, et al.
Published: (2025)
by: Chen, Yixiao, 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)
MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE
by: Huang, Zongle, et al.
Published: (2025)
by: Huang, Zongle, et al.
Published: (2025)
STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation
by: Wang, Yiming, et al.
Published: (2025)
by: Wang, Yiming, et al.
Published: (2025)
SonicMoE: Accelerating MoE with IO and Tile-aware Optimizations
by: Guo, Wentao, et al.
Published: (2025)
by: Guo, Wentao, et al.
Published: (2025)
WAM-Diff: A Masked Diffusion VLA Framework with MoE and Online Reinforcement Learning for Autonomous Driving
by: Xu, Mingwang, et al.
Published: (2025)
by: Xu, Mingwang, et al.
Published: (2025)
MxMoE: Mixed-precision Quantization for MoE with Accuracy and Performance Co-Design
by: Duanmu, Haojie, et al.
Published: (2025)
by: Duanmu, Haojie, et al.
Published: (2025)
Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network
by: Zhang, Weijia, et al.
Published: (2023)
by: Zhang, Weijia, et al.
Published: (2023)
Expert Divergence Learning for MoE-based Language Models
by: Li, Jiaang, et al.
Published: (2026)
by: Li, Jiaang, et al.
Published: (2026)
An Adversarial Learning Approach to Irregular Time-Series Forecasting
by: Nam, Heejeong, et al.
Published: (2024)
by: Nam, Heejeong, et al.
Published: (2024)
Probabilistic Learning of Multivariate Time Series with Temporal Irregularity
by: Li, Yijun, et al.
Published: (2023)
by: Li, Yijun, et al.
Published: (2023)
Temporal Dynamic Embedding for Irregularly Sampled Time Series
by: Kim, Mincheol, et al.
Published: (2025)
by: Kim, Mincheol, et al.
Published: (2025)
Heteroscedastic Temporal Variational Autoencoder For Irregular Time Series
by: Shukla, Satya Narayan, et al.
Published: (2021)
by: Shukla, Satya Narayan, et al.
Published: (2021)
Causal Discovery for Irregularly Time Series with Consistency Guarantees
by: Li, Weihong, et al.
Published: (2025)
by: Li, Weihong, et al.
Published: (2025)
SD-MoE: Spectral Decomposition for Effective Expert Specialization
by: Huang, Ruijun, et al.
Published: (2026)
by: Huang, Ruijun, et al.
Published: (2026)
Similar Items
-
TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts
by: Huang, Yu-Hao, et al.
Published: (2025) -
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
by: Shi, Xiaoming, et al.
Published: (2024) -
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process
by: Fan, Xinyao, 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) -
Latent Laplace Diffusion for Irregular Multivariate Time Series
by: You, Zinuo, et al.
Published: (2026)