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Mixture-of-Experts

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

https://github.com/zhujiagang/gating-ConvNet-code

공식 코드

Learning Gating ConvNet for Two-Stream based Methods in Action Recognition

https://github.com/zhujiapeng/aurora

공식 코드

Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis

https://github.com/zhujiwei111/space

공식 코드

SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model

https://github.com/zjwang21/moe-lpr

공식 코드

MoE-LPR: Multilingual Extension of Large Language Models through Mixture-of-Experts with Language Priors Routing

https://github.com/zongqianli/prompt-tuning-survey

공식 코드

A Survey on Prompt Tuning

https://github.com/zongqianli/pt-moe

공식 코드

PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning

https://github.com/zwang78/moediff-sr

공식 코드

MoEDiff-SR: Mixture of Experts-Guided Diffusion Model for Region-Adaptive MRI Super-Resolution

https://github.com/zyang1580/dcr

공식 코드

Addressing Confounding Feature Issue for Causal Recommendation

https://github.com/zyphra/blackmamba

공식 코드

BlackMamba: Mixture of Experts for State-Space Models

https://github.com/zyy-2001/cola

공식 코드

CoLA: Collaborative Low-Rank Adaptation

https://gitlab.com/afagarap/kwta-ensemble

공식 코드

k-Winners-Take-All Ensemble Neural Network

https://github.com/2023-MindSpore-4/Code8/tree/main/advanced_east

DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale

https://github.com/Adlik/model_optimizer

Distilling the Knowledge in a Neural Network

https://github.com/Andy1314Chen/PaddleRec

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

https://github.com/BrianPulfer/PapersReimplementations

Fast Feedforward Networks

https://github.com/Deci-AI/super-gradients

Distilling the Knowledge in a Neural Network

https://github.com/DonatellaGenovese/Mixture-of-Expert-Graph-Transformer

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection

https://github.com/DunZhang/KnowledgeDistillation

Distilling the Knowledge in a Neural Network

https://github.com/Entodi/fusion

Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease

https://github.com/FlagAI-Open/Aquila-MoE

AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies

https://github.com/Irtza/Keras_model_compression

Distilling the Knowledge in a Neural Network

https://github.com/IsaacRodgz/ConcatBERT

Gated Multimodal Units for Information Fusion

https://github.com/IsaacRodgz/GMU-Baseline

Gated Multimodal Units for Information Fusion

https://github.com/IsaacRodgz/mmbt_experiments

Gated Multimodal Units for Information Fusion

https://github.com/IsaacRodgz/multimodal-transformers-movies

Gated Multimodal Units for Information Fusion

https://github.com/JunzWu/Distilling-the-Knowledge-in-a-Neural-Network

Distilling the Knowledge in a Neural Network

https://github.com/KU-CVLAB/MoA

Sparse Mixture-of-Experts are Domain Generalizable Learners

https://github.com/KaiyuYue/mgd

Distilling the Knowledge in a Neural Network

https://github.com/KellyYutongHe/Knowledge-Distillation-Net-with-Swish

Distilling the Knowledge in a Neural Network

https://github.com/Leeroo-AI/mergoo

MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models

https://github.com/MS-P3/code3/tree/main/jamba

Jamba: A Hybrid Transformer-Mamba Language Model

https://github.com/MS-P3/code3/tree/main/jetmoe

JetMoE: Reaching Llama2 Performance with 0.1M Dollars

https://github.com/MS-P3/code5/tree/main/jetmoe

JetMoE: Reaching Llama2 Performance with 0.1M Dollars

https://github.com/MasLiang/Learning-without-Forgetting-using-Pytorch

Distilling the Knowledge in a Neural Network

https://github.com/MinJunKang/CIFAR-100--Knowledge-Distillation-with-Augmented-Data

Distilling the Knowledge in a Neural Network

https://github.com/MinJunKang/ImageNet-Knowledge-Distillation

Distilling the Knowledge in a Neural Network

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