https://github.com/rucaibox/mpo
공식 코드Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models
Papers with Code 아카이브
Mixture-of-Experts
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/rucaibox/mpoe
공식 코드Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models
https://github.com/rucaibox/tedrec
공식 코드Sequence-level Semantic Representation Fusion for Recommender Systems
https://github.com/rucaibox/unisrec
공식 코드Towards Universal Sequence Representation Learning for Recommender Systems
https://github.com/rufaim/routing-by-memory
공식 코드https://github.com/sade-adrien/stelocoder
공식 코드SteloCoder: a Decoder-Only LLM for Multi-Language to Python Code Translation
https://github.com/salesforce/hydra-sum
공식 코드HydraSum: Disentangling Stylistic Features in Text Summarization using Multi-Decoder Models
https://github.com/sandeep-sm/live-sc
공식 코드Subjective and Objective Analysis of Indian Social Media Video Quality
https://github.com/sftekin/h3fusion
공식 코드$H^3$Fusion: Helpful, Harmless, Honest Fusion of Aligned LLMs
https://github.com/sgalkina/poe-vaes
공식 코드Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts
https://github.com/shaier/comet
공식 코드https://github.com/shallowdream204/lora-ir
공식 코드LoRA-IR: Taming Low-Rank Experts for Efficient All-in-One Image Restoration
https://github.com/sharc-lab/edge-moe
공식 코드https://github.com/shawnhuang497/medplib
공식 코드Towards a Multimodal Large Language Model with Pixel-Level Insight for Biomedicine
https://github.com/shawntan/scattermoe
공식 코드https://github.com/shenmishajing/lifted
공식 코드Multimodal Clinical Trial Outcome Prediction with Large Language Models
https://github.com/shi-labs/cumo
공식 코드CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts
https://github.com/shibshib/Moe-VRD
공식 코드https://github.com/shufangxun/llava-mod
공식 코드https://github.com/shwai-he/meo
공식 코드Merging Experts into One: Improving Computational Efficiency of Mixture of Experts
https://github.com/shwai-he/pad-net
공식 코드https://github.com/simiaozuo/moebert
공식 코드MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation
https://github.com/simsal0r/mixture-of-decision-trees
공식 코드Mixture of Decision Trees for Interpretable Machine Learning
https://github.com/sitzikbs/Nesti-Net
공식 코드Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks
https://github.com/skyworkai/moe-plus-plus
공식 코드MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
https://github.com/skyworkai/moh
공식 코드https://github.com/sonyresearch/micro_diffusion
공식 코드Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget
https://github.com/sootung/askchart
공식 코드AskChart: Universal Chart Understanding through Textual Enhancement
https://github.com/sotarokaneda/mlcarbon
공식 코드LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models
https://github.com/souljaboy764/moveint
공식 코드MoVEInt: Mixture of Variational Experts for Learning Human-Robot Interactions from Demonstrations
https://github.com/spcl/smoe
공식 코드https://github.com/spico197/moe-sft
공식 코드Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts
https://github.com/sqlcow/TITAN
공식 코드A Time Series is Worth Five Experts: Heterogeneous Mixture of Experts for Traffic Flow Prediction
https://github.com/stanford-futuredata/megablocks
공식 코드MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
https://github.com/starmemda/CAMoE
공식 코드Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
https://github.com/starmemda/camow
공식 코드Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.