https://github.com/writer/writing-in-the-margins
공식 코드Writing in the Margins: Better Inference Pattern for Long Context Retrieval
Papers with Code 아카이브
Retrieval
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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/wrld/PRoGAN
공식 코드PREGAN: Pose Randomization and Estimation for Weakly Paired Image Style Translation
https://github.com/wsdm-cup-2017/lettuce
공식 코드Ensemble of Neural Classifiers for Scoring Knowledge Base Triples
https://github.com/wssun/eacs
공식 코드An Extractive-and-Abstractive Framework for Source Code Summarization
https://github.com/wtma/PR-Embedding
공식 코드Conversational Word Embedding for Retrieval-Based Dialog System
https://github.com/wtyhub/MuseNet
공식 코드Multiple-environment Self-adaptive Network for Aerial-view Geo-localization
https://github.com/wujwyi/pa-rag
공식 코드PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization
https://github.com/wuning0929/ccp_ijcai22
공식 코드Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval
https://github.com/wusiwei0410/scimmir
공식 코드SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval
https://github.com/wutaiqiang/ger-wsdm2023
공식 코드https://github.com/wvangansbeke/Revisiting-Contrastive-SSL
공식 코드Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations
https://github.com/wwxu21/AMR-SG
공식 코드https://github.com/wyl-willing/MindMap
공식 코드MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models
https://github.com/wyu97/TransTD
공식 코드https://github.com/wyu97/raco
공식 코드Retrieval Augmentation for Commonsense Reasoning: A Unified Approach
https://github.com/wzhings/itmafa
공식 코드Enhancing Image-Text Matching with Adaptive Feature Aggregation
https://github.com/wzq016/pine
공식 코드Eliminating Position Bias of Language Models: A Mechanistic Approach
https://github.com/wzzheng/idml
공식 코드https://github.com/x-lance/neusym-rag
공식 코드NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering
https://github.com/x-lance/weblm
공식 코드Hierarchical Multimodal Pre-training for Visually Rich Webpage Understanding
https://github.com/x-zb/pedb
공식 코드An Empirical Analysis of Parameter-Efficient Methods for Debiasing Pre-Trained Language Models
https://github.com/xandery-geek/SAAT
공식 코드Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
https://github.com/xfactlab/emnlp2023-damaging-retrieval
공식 코드https://github.com/xfactlab/emnlp2023-knowledge-corpus-error
공식 코드https://github.com/xfactlab/i0t
공식 코드I0T: Embedding Standardization Method Towards Zero Modality Gap
https://github.com/xgl-git/ns-ir-main
공식 코드Logical Consistency is Vital: Neural-Symbolic Information Retrieval for Negative-Constraint Queries
https://github.com/xhluca/bm25s
공식 코드BM25S: Orders of magnitude faster lexical search via eager sparse scoring
https://github.com/xialeiliu/CrowdCountingCVPR18
공식 코드Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
https://github.com/xiami2019/UAR
공식 코드https://github.com/xiangyu-mm/unifashion
공식 코드UniFashion: A Unified Vision-Language Model for Multimodal Fashion Retrieval and Generation
https://github.com/xianjun-yang/pcmsp
공식 코드MatKB: Semantic Search for Polycrystalline Materials Synthesis Procedures
https://github.com/xianyongqin/few-shot-video-classification
공식 코드Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation
https://github.com/xiaofei05/tsst
공식 코드https://github.com/xiaoleihuang/useremb_explainable
공식 코드https://github.com/xiaomi-research/dasheng-glap
공식 코드GLAP: General contrastive audio-text pretraining across domains and languages
https://github.com/xiaowu0162/low-resource-kpgen
공식 코드Representation Learning for Resource-Constrained Keyphrase Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.