https://github.com/joesuk/nonstationary-duel
공식 코드When Can We Track Significant Preference Shifts in Dueling Bandits?
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 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/joez17/chatsearch
공식 코드ChatSearch: a Dataset and a Generative Retrieval Model for General Conversational Image Retrieval
https://github.com/johannesliu/music-genre-classification
공식 코드MATT: A Multiple-instance Attention Mechanism for Long-tail Music Genre Classification
https://github.com/john-ai-lab/rapid
공식 코드Long-Context Inference with Retrieval-Augmented Speculative Decoding
https://github.com/johncaged/OPT_Questioner
공식 코드Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner
https://github.com/johncolddd/ppn
공식 코드A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging
https://github.com/joisino/seafaring
공식 코드https://github.com/joisino/tiara
공식 코드https://github.com/jonasrothfuss/DeepEpisodicMemory
공식 코드https://github.com/jonathanzha47/smartfinrag
공식 코드SMARTFinRAG: Interactive Modularized Financial RAG Benchmark
https://github.com/jonhue/activeft
공식 코드Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs
https://github.com/jonkahana/dcodr
공식 코드A Contrastive Objective for Learning Disentangled Representations
https://github.com/jonlin223/raccoon-geocoding
공식 코드https://github.com/jordane95/dual-cross-encoder
공식 코드Learning Diverse Document Representations with Deep Query Interactions for Dense Retrieval
https://github.com/jordane95/procqa
공식 코드ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search
https://github.com/jou2024/EXPRAG
공식 코드Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA
https://github.com/jpainam/SLS_ReID
공식 코드Sparse Label Smoothing Regularization for Person Re-Identification
https://github.com/jpickard1/brad
공식 코드https://github.com/jpthu17/HBI
공식 코드Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning
https://github.com/jpthu17/dicosa
공식 코드Text-Video Retrieval with Disentangled Conceptualization and Set-to-Set Alignment
https://github.com/jpthu17/diffusionret
공식 코드DiffusionRet: Generative Text-Video Retrieval with Diffusion Model
https://github.com/jpthu17/emcl
공식 코드Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
https://github.com/jrnlp/multi-eup
공식 코드https://github.com/jsalt2024-evaluating-llms-for-astronomy/astro-arxiv-bot
공식 코드Designing an Evaluation Framework for Large Language Models in Astronomy Research
https://github.com/jspenmar/DejaVu_Features
공식 코드Same Features, Different Day: Weakly Supervised Feature Learning for Seasonal Invariance
https://github.com/jssprz/video_captioning_datasets
공식 코드https://github.com/jsunn-y/care
공식 코드CARE: a Benchmark Suite for the Classification and Retrieval of Enzymes
https://github.com/ju-chen/Efficient-Prompt
공식 코드Prompting Visual-Language Models for Efficient Video Understanding
https://github.com/juexinlin/dense_retrieval_relevance_filter
공식 코드https://github.com/juhannam/deepbof
공식 코드https://github.com/julian-risch/PatentMatch
공식 코드PatentMatch: A Dataset for Matching Patent Claims & Prior Art
https://github.com/julian-urbano/sigir2019-statistical
공식 코드https://github.com/jun-jie-huang/otter
공식 코드Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA
https://github.com/junhocho/paired-glyph-matching
공식 코드https://github.com/junj2ejj/cs-lol
공식 코드CS-lol: a Dataset of Viewer Comment with Scene in E-sports Live-streaming
https://github.com/junyangwang0410/knight
공식 코드From Association to Generation: Text-only Captioning by Unsupervised Cross-modal Mapping
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.