Papers with Code 아카이브
Information Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 33
- 보존된 평가 기록
- 17
Information retrieval is the task of ranking a list of documents or search results in response to a query
<span style="color:grey; opacity: 0.6">( Image credit: sudhanshumittal )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/raruidol/budget-am
공식 코드https://github.com/rea1991/SHREC2021
공식 코드https://github.com/realtimeqa/realtimeqa_public
공식 코드https://github.com/reddragon/efficient-dl-survey-paper
공식 코드Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better
https://github.com/reembinhezam/grlstop
공식 코드A Generalised and Adaptable Reinforcement Learning Stopping Method
https://github.com/reembinhezam/rlstop
공식 코드https://github.com/reembinhezam/tar_stopping_cp_clf
공식 코드https://github.com/reembinhezam/tar_stopping_point_processes
공식 코드Stopping Methods for Technology Assisted Reviews based on Point Processes
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/retrocirce/tonet
공식 코드TONet: Tone-Octave Network for Singing Melody Extraction from Polyphonic Music
https://github.com/richielee93/eder
공식 코드EDeR: A Dataset for Exploring Dependency Relations Between Events
https://github.com/rishub-tamirisa/tamper-resistance
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/rlacombe/climatex
공식 코드ClimateX: Do LLMs Accurately Assess Human Expert Confidence in Climate Statements?
https://github.com/rlitschk/UnsupCLIR
공식 코드Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only
https://github.com/rmahari/lepard
공식 코드https://github.com/roan-schellingerhout/evaluating_job_recommendations
공식 코드Creating Healthy Friction: Determining Stakeholder Requirements of Job Recommendation Explanations
https://github.com/rosenberg37/oprf
공식 코드Offline Pseudo Relevance Feedback for Efficient and Effective Single-pass Dense Retrieval
https://github.com/rosewang2008/backtracing
공식 코드https://github.com/rowedenny/MULTR
공식 코드https://github.com/rowitzou/dc-match
공식 코드Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents
https://github.com/rsvp-ai/bertserini
공식 코드https://github.com/ruathudo/tfw2v
공식 코드TFW2V: An Enhanced Document Similarity Method for the Morphologically Rich Finnish Language
https://github.com/ruc-gsai/yulan-ir
공식 코드RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
https://github.com/ruc-nlpir/genir-survey
공식 코드From Matching to Generation: A Survey on Generative Information Retrieval
https://github.com/ruc-nlpir/llm4ir-survey
공식 코드https://github.com/rucaibox/simpledeepsearcher
공식 코드SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis
https://github.com/ruyue0001/backdoor_dpr
공식 코드Whispers in Grammars: Injecting Covert Backdoors to Compromise Dense Retrieval Systems
https://github.com/sabeesh90/MosaicML_Augmentations_Efficient_Deep_Learning_MLDS_2022
공식 코드https://github.com/sadrultoaha/signboard-detection
공식 코드Automatic Signboard Detection and Localization in Densely Populated Developing Cities
https://github.com/sageselab/ui-bug-localization-study
공식 코드On Using GUI Interaction Data to Improve Text Retrieval-based Bug Localization
https://github.com/saifulhaq95/indicirsuite
공식 코드IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian Languages
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.