https://github.com/Hannibal046/xRAG
공식 코드xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/HarleyHK/FARF
공식 코드https://github.com/HarrieO/2020topkunbiasedltr
공식 코드https://github.com/HarrieO/BalancingSpeedQualityOLTR
공식 코드Balancing Speed and Quality in Online Learning to Rank for Information Retrieval
https://github.com/HassounLab/MADGEN
공식 코드https://github.com/Hayeonbang/PIAST
공식 코드PIAST: A Multimodal Piano Dataset with Audio, Symbolic and Text
https://github.com/HighwayWu/SIFT-Reconstruction
공식 코드Privacy Leakage of SIFT Features via Deep Generative Model based Image Reconstruction
https://github.com/HolaYan/SUCEA
공식 코드https://github.com/HongruCai/SLGR
공식 코드Exploring Training and Inference Scaling Laws in Generative Retrieval
https://github.com/HongyangLL/M3-JEPA
공식 코드M3-Jepa: Multimodal Alignment via Multi-directional MoE based on the JEPA framework
https://github.com/HopLee6/VJMHT-PyTorch
공식 코드Video Joint Modelling Based on Hierarchical Transformer for Co-summarization
https://github.com/Hua-YS/Prototype-based-Memory-Network
공식 코드Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks
https://github.com/HuiChen24/IMRAM
공식 코드IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
https://github.com/HuiGuanLab/ms-sl
공식 코드https://github.com/Huntersxsx/MGPN
공식 코드You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos
https://github.com/IAAR-Shanghai/Meta-Chunking
공식 코드MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation System
https://github.com/IAAR-Shanghai/QAEncoder
공식 코드QAEncoder: Towards Aligned Representation Learning in Question Answering System
https://github.com/IBM/ColBERT-practical
공식 코드Fast and Light-Weight Answer Text Retrieval in Dialogue Systems
https://github.com/IBM/Retrieval-Enhanced-Transformer-Little
공식 코드https://github.com/IBM/retrieve-write-slot-filling
공식 코드https://github.com/IBM/row-column-intersection
공식 코드https://github.com/IBM/twitter-customer-care-document-prediction
공식 코드Conversational Document Prediction to Assist Customer Care Agents
https://github.com/IDEA-XL/RAPM
공식 코드Rethinking Text-based Protein Understanding: Retrieval or LLM?
https://github.com/IGLICT/RisaNET
공식 코드RISA-Net: Rotation-Invariant Structure-Aware Network for Fine-Grained 3D Shape Retrieval
https://github.com/INK-USC/DualRE
공식 코드Learning Dual Retrieval Module for Semi-supervised Relation Extraction
https://github.com/INK-USC/ReCross
공식 코드Unsupervised Cross-Task Generalization via Retrieval Augmentation
https://github.com/IPL-UV/mlregocean
공식 코드Retrieval of Case 2 Water Quality Parameters with Machine Learning
https://github.com/Irene323/GFTE
공식 코드https://github.com/IrvingMeng/LCE
공식 코드https://github.com/J-zin/SNUH
공식 코드https://github.com/J22Melody/fairseq
공식 코드SignCLIP: Connecting Text and Sign Language by Contrastive Learning
https://github.com/JabezNzomo99/claim-matching-robustness
공식 코드https://github.com/James-Yip/TGODC-DKRN
공식 코드Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation
https://github.com/James-Yuichi-Sato/MLSYS-2022-Video-Pipeline
공식 코드https://github.com/JamesGlare/Holo_gen_models
공식 코드Digital phase-only holography using deep conditional generative models
https://github.com/JasonForJoy/BRIEF
공식 코드BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.