https://github.com/reddragon/efficient-dl-survey-paper
공식 코드Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 11
- 보존된 평가 기록
- 19
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/regnlp/obligationclassifier
공식 코드RIRAG: Regulatory Information Retrieval and Answer Generation
https://github.com/regnlp/obliqadataset
공식 코드RIRAG: Regulatory Information Retrieval and Answer Generation
https://github.com/rendanim/mphayaner
공식 코드https://github.com/reneeye/const
공식 코드https://github.com/renshuhuai-andy/testa
공식 코드TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding
https://github.com/rerv/uniadapter
공식 코드UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
https://github.com/research2020-1/DSTDH
공식 코드Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval
https://github.com/researchmm/TracKit
공식 코드Towards Accurate Pixel-wise Object Tracking by Attention Retrieval
https://github.com/researchmm/soho
공식 코드Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning
https://github.com/retrocirce/tonet
공식 코드TONet: Tone-Octave Network for Singing Melody Extraction from Polyphonic Music
https://github.com/rezaqorbani/semla
공식 코드Semantic Library Adaptation: LoRA Retrieval and Fusion for Open-Vocabulary Semantic Segmentation
https://github.com/rgu-iit-bt/cbr-for-legal-rag
공식 코드https://github.com/ricedsp/prDeep
공식 코드https://github.com/richard-peng-xia/mmed-rag
공식 코드MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models
https://github.com/richard-peng-xia/rule
공식 코드RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models
https://github.com/richardzhu123/ferns
공식 코드https://github.com/richielee93/eder
공식 코드EDeR: A Dataset for Exploring Dependency Relations Between Events
https://github.com/riedlerm/multimodal_rag_for_industry
공식 코드Beyond Text: Optimizing RAG with Multimodal Inputs for Industrial Applications
https://github.com/ritamargherita/llms-topic-classification
공식 코드Zero-Shot Topic Classification of Column Headers: Leveraging LLMs for Metadata Enrichment
https://github.com/ritaramo/extra
공식 코드https://github.com/ritaramo/lmcap
공식 코드LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model Prompting
https://github.com/ritaramo/smallcap
공식 코드SmallCap: Lightweight Image Captioning Prompted with Retrieval Augmentation
https://github.com/ritaranx/bmretriever
공식 코드BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers
https://github.com/ritaranx/collab-rag
공식 코드https://github.com/ritaranx/ram-ehr
공식 코드RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records
https://github.com/ritaranx/wander
공식 코드Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training
https://github.com/rizwan09/redcoder
공식 코드https://github.com/rjagerman/glint
공식 코드Computing Web-scale Topic Models using an Asynchronous Parameter Server
https://github.com/rjagerman/tois2019-safe-exploration-algorithm
공식 코드https://github.com/rkthomps/coq-modeling
공식 코드Rango: Adaptive Retrieval-Augmented Proving for Automated Software Verification
https://github.com/rlacombe/climatex
공식 코드ClimateX: Do LLMs Accurately Assess Human Expert Confidence in Climate Statements?
https://github.com/rlitschk/EncoderCLIR
공식 코드Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval
https://github.com/rlitschk/UnsupCLIR
공식 코드Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only
https://github.com/rlitschk/modularclir
공식 코드Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval
https://github.com/rmahari/lepard
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.