Papers with Code 아카이브

Mixture-of-Experts

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

0

보존된 평가 기록

0

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/fsoft-aic/competesmoe

공식 코드

CompeteSMoE -- Statistically Guaranteed Mixture of Experts Training via Competition

https://github.com/fundamentalvision/Uni-Perceiver

공식 코드

Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs

https://github.com/gatech-eic/shiftaddvit

공식 코드

ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer

https://github.com/gcyzsl/mola

공식 코드

Higher Layers Need More LoRA Experts

https://github.com/genezc/minimoe

공식 코드

Lifting the Curse of Capacity Gap in Distilling Language Models

https://github.com/gengzezhou/same

공식 코드

SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of Experts

https://github.com/giangdip2410/hyperrouter

공식 코드

HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts

https://github.com/google-research/google-research

공식 코드

Sparse Mixers: Combining MoE and Mixing to build a more efficient BERT

https://github.com/google-research/google-research/tree/master/dselect_k_moe

공식 코드

DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning

https://github.com/google-research/vmoe

공식 코드

Scaling Vision with Sparse Mixture of Experts

https://github.com/guobbin/PFL-MoE

공식 코드

PFL-MoE: Personalized Federated Learning Based on Mixture of Experts

https://github.com/guqiqi/Samoyeds

공식 코드

Samoyeds: Accelerating MoE Models with Structured Sparsity Leveraging Sparse Tensor Cores

https://github.com/haiduo/Jakiro

공식 코드

Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE

https://github.com/haoyifu1993/cbmose

공식 코드

Covariate-guided Bayesian mixture model for multivariate time series

https://github.com/haoyiq114/CROSS

공식 코드

Multimodal Cultural Safety: Evaluation Frameworks and Alignment Strategies

https://github.com/hdong920/griffin

공식 코드

Prompt-prompted Adaptive Structured Pruning for Efficient LLM Generation

https://github.com/he-h/HuMob

공식 코드

ST-MoE-BERT: A Spatial-Temporal Mixture-of-Experts Framework for Long-Term Cross-City Mobility Prediction

https://github.com/helpstonex/crazyara

공식 코드

Checkmating One, by Using Many: Combining Mixture of Experts with MCTS to Improve in Chess

https://github.com/hhexiy/opponent

공식 코드

Opponent Modeling in Deep Reinforcement Learning

https://github.com/hidream-ai/hidream-e1

공식 코드

HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer

https://github.com/hidream-ai/hidream-i1

공식 코드

HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer

https://github.com/hitsz-tmg/umoe-scaling-unified-multimodal-llms

공식 코드

Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts

https://github.com/hkuds/anygraph

공식 코드

AnyGraph: Graph Foundation Model in the Wild

https://github.com/honee-w/u-sam

공식 코드

U-SAM: An audio language Model for Unified Speech, Audio, and Music Understanding

https://github.com/hrdhrd/MCO-E-Net

공식 코드

Multi-modal Collaborative Optimization and Expansion Network for Event-assisted Single-eye Expression Recognition

https://github.com/hrlics/lite

공식 코드

LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models

https://github.com/huang50213/AIM-Fewshot-Continual

공식 코드

Few-Shot and Continual Learning with Attentive Independent Mechanisms

https://github.com/hunter-ddm/stablemoe

공식 코드

StableMoE: Stable Routing Strategy for Mixture of Experts

https://github.com/hyunwookl/testam

공식 코드

TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts

https://github.com/ibm/moduleformer

공식 코드

ModuleFormer: Modularity Emerges from Mixture-of-Experts

https://github.com/ictnlp/moe-waitk

공식 코드

Universal Simultaneous Machine Translation with Mixture-of-Experts Wait-k Policy

https://github.com/icuraslw/official-repository-of-confsmoe

공식 코드

Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate

https://github.com/idea-isail-lab-uiuc/resmoe

공식 코드

ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restoration

https://github.com/ieit-yuan/yuan2.0-m32

공식 코드

Yuan 2.0-M32: Mixture of Experts with Attention Router

https://github.com/iffsid/mmvae

공식 코드

Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models

https://github.com/igarashiakatuki/cnmbert

공식 코드

CNMBERT: A Model for Converting Hanyu Pinyin Abbreviations to Chinese Characters

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.