Cover Image

L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yang, Minghao, Togo, Ren, Li, Guang, Ogawa, Takahiro, Haseyama, Miki
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adaptive Shared Experts with LoRA-Based Mixture of Experts for Multi-Task Learning
by: Yang, Minghao, et al.
Published: (2025)

Importance-Aware Adaptive Dataset Distillation
by: Li, Guang, et al.
Published: (2024)

Generative Dataset Distillation Based on Self-knowledge Distillation
by: Li, Longzhen, et al.
Published: (2025)

Generative Dataset Distillation: Balancing Global Structure and Local Details
by: Li, Longzhen, et al.
Published: (2024)

Dual-Model Weight Selection and Self-Knowledge Distillation for Medical Image Classification
by: Tsutsumi, Ayaka, et al.
Published: (2025)

Generative Dataset Distillation Based on Diffusion Model
by: Su, Duo, et al.
Published: (2024)

RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images
by: Li, Guang, et al.
Published: (2022)

Predictive but Not Plannable: RC-aux for Latent World Models
by: Li, Wenyuan, et al.
Published: (2026)

Hyperbolic Dataset Distillation
by: Li, Wenyuan, et al.
Published: (2025)

Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory
by: Li, Mingzhuo, et al.
Published: (2025)

Closed-Form Linear-Probe Dataset Distillation for Pre-trained Vision Models
by: Peng, Bincheng, et al.
Published: (2026)

Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach
by: Togo, Taro, et al.
Published: (2024)

Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering
by: Zhu, He, et al.
Published: (2024)

Task-Specific Generative Dataset Distillation with Difficulty-Guided Sampling
by: Li, Mingzhuo, et al.
Published: (2025)

Foreground-Aware Dataset Distillation via Dynamic Patch Selection
by: Li, Longzhen, et al.
Published: (2026)

Cross-domain Multi-step Thinking: Zero-shot Fine-grained Traffic Sign Recognition in the Wild
by: Gan, Yaozong, et al.
Published: (2024)

Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition
by: Gan, Yaozong, et al.
Published: (2024)

MMT-BERT: Chord-aware Symbolic Music Generation Based on Multitrack Music Transformer and MusicBERT
by: Zhu, Jinlong, et al.
Published: (2024)

Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustness
by: Tasai, Ren, et al.
Published: (2025)

Objectness Similarity: Capturing Object-Level Fidelity in 3D Scene Evaluation
by: Uchida, Yuiko, et al.
Published: (2025)

SAS: Semantic-aware Sampling for Generative Dataset Distillation
by: Li, Mingzhuo, et al.
Published: (2026)

Information-Guided Diffusion Sampling for Dataset Distillation
by: Ye, Linfeng, et al.
Published: (2025)

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)

Multilingual Routing in Mixture-of-Experts
by: Bandarkar, Lucas, et al.
Published: (2025)

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection
by: Zhan, Zheng, et al.
Published: (2025)

Soft-Label Anonymous Gastric X-ray Image Distillation
by: Li, Guang, et al.
Published: (2021)

L-MoE: End-to-End Training of a Lightweight Mixture of Low-Rank Adaptation Experts
by: Ji, Shihao, et al.
Published: (2025)

HELLoRA: Hot Experts Layer-Level Low-Rank Adaptation for Mixture-of-Experts Models
by: Wei, Jia, et al.
Published: (2026)

RouteHijack: Routing-Aware Attack on Mixture-of-Experts LLMs
by: Xu, Zhiyuan, et al.
Published: (2026)

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

A Stronger Mixture of Low-Rank Experts for Fine-Tuning Foundation Models
by: Sun, Mengyang, et al.
Published: (2025)

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

MoR: Mixture of Ranks for Low-Rank Adaptation Tuning
by: Tang, Chuanyu, et al.
Published: (2024)

Not All Models Suit Expert Offloading: On Local Routing Consistency of Mixture-of-Expert Models
by: Liang, Jingcong, et al.
Published: (2025)

Neural Inhibition Improves Dynamic Routing and Mixture of Experts
by: Zou, Will Y., et al.
Published: (2025)

Variational Routing: A Scalable Bayesian Framework for Calibrated Mixture-of-Experts Transformers
by: Li, Albus Yizhuo, et al.
Published: (2026)

ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts
by: Zhao, Heng, et al.
Published: (2026)

SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models
by: Tang, Anke, et al.
Published: (2024)

Geometric Mixture-of-Experts with Curvature-Guided Adaptive Routing for Graph Representation Learning
by: Cao, Haifang, et al.
Published: (2026)

Mixture-of-Subspaces in Low-Rank Adaptation
by: Wu, Taiqiang, et al.
Published: (2024)