https://github.com/microsoft/stochastic-mixture-of-experts
공식 코드Taming Sparsely Activated Transformer with Stochastic Experts
Papers with Code 아카이브
Mixture-of-Experts
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https://github.com/microsoft/tutel
공식 코드https://github.com/microsoft/unilm
공식 코드https://github.com/microsoft/wina
공식 코드WINA: Weight Informed Neuron Activation for Accelerating Large Language Model Inference
https://github.com/mikecovlee/DynMoLE
공식 코드DynMoLE: Boosting Mixture of LoRA Experts Fine-Tuning with a Hybrid Routing Mechanism
https://github.com/mikecovlee/mLoRA
공식 코드MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts
https://github.com/mimbres/yourmt3
공식 코드https://github.com/mingyuan-zhang/FineMoGen
공식 코드FineMoGen: Fine-Grained Spatio-Temporal Motion Generation and Editing
https://github.com/minhchuyentoancbn/moe_promptcl
공식 코드https://github.com/minimax-ai/minimax-m1
공식 코드MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
https://github.com/mirah-official/empathetic-chatbot-asem
공식 코드ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling
https://github.com/ml-ml/link-moe
공식 코드https://github.com/model-glue/model-glue
공식 코드Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild
https://github.com/modelscope/adaseq
공식 코드Named Entity and Relation Extraction with Multi-Modal Retrieval
https://github.com/moe-inf/awesome-moe-inference
공식 코드A Survey on Inference Optimization Techniques for Mixture of Experts Models
https://github.com/moonshotai/kimi-vl
공식 코드https://github.com/moonshotai/moba
공식 코드https://github.com/morelife2017/alphalora
공식 코드AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality
https://github.com/mr-bamboo/mfg-hmoe
공식 코드Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution
https://github.com/mrluin/unirestorer
공식 코드https://github.com/mryab/learning-at-home
공식 코드Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts
https://github.com/murinj/big-moe
공식 코드BIG-MoE: Bypass Isolated Gating MoE for Generalized Multimodal Face Anti-Spoofing
https://github.com/mxu34/mbrl-gpmm
공식 코드Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
https://github.com/myshell-ai/jetmoe
공식 코드https://github.com/n3il666/meta-dmoe
공식 코드Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts
https://github.com/nazaninmn/mosare
공식 코드Towards Robust Multimodal Representation: A Unified Approach with Adaptive Experts and Alignment
https://github.com/neelkanwal/equipping-computational-pathology-systems-with-artifact-processing-pipeline
공식 코드https://github.com/nguyendohoangkhoiUF/Swift-Hydra
공식 코드Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba Models
https://github.com/nomic-ai/contrastors
공식 코드https://github.com/nowazrabbani/pmoe_cnn
공식 코드https://github.com/ntunlp/summareranker
공식 코드SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization
https://github.com/nus-hpc-ai-lab/dynamic-tuning
공식 코드Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation
https://github.com/nusnlp/moece
공식 코드Efficient and Interpretable Grammatical Error Correction with Mixture of Experts
https://github.com/nvidia/modulus-sym
공식 코드https://github.com/onur4229/svsl_lmoe
공식 코드Specializing Versatile Skill Libraries using Local Mixture of Experts
https://github.com/opensparsellms/linear-moe
공식 코드Linear-MoE: Linear Sequence Modeling Meets Mixture-of-Experts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.