Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/rachtibat/lrp-for-transformers
https://github.com/rafiahmed40/stack-adverserial-network
Learning Deep Representations of Fine-grained Visual Descriptions
https://github.com/rahmanidashti/acqsurvey
Asking Clarifying Questions in Open-Domain Information-Seeking Conversations
https://github.com/rajaswa/DRIFT
https://github.com/raminmh/liquid_time_constant_networks
https://github.com/raojay7/cnnimageretrieval-pytorch
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
https://github.com/raotnameh/AML_Course/tree/main/project
Generalized Product Quantization Network for Semi-supervised Image Retrieval
https://github.com/raphaelsty/mkb
Inductive Entity Representations from Text via Link Prediction
https://github.com/rapidsai/cuvs
https://github.com/rasoolims/YaraParser
https://github.com/raymondhs/fairseq-laser
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
https://github.com/reczoo/FuxiCTR
End-to-End User Behavior Retrieval in Click-Through RatePrediction Model
https://github.com/reczoo/FuxiCTR/tree/main/model_zoo/LongCTR
https://github.com/reedscot/cvpr2016
Learning Deep Representations of Fine-grained Visual Descriptions
https://github.com/refuel-ai/autolabel
AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators
https://github.com/relic-yuexi/agentcourt
DeliLaw: A Chinese Legal Counselling System Based on a Large Language Model
https://github.com/reml-group/deliberation-on-priors
Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning
https://github.com/rightlit/StackGAN-v2-rev
Learning Deep Representations of Fine-grained Visual Descriptions
https://github.com/rishi00789/Food-AI
https://github.com/ritchie46/lsh-rs
https://github.com/ritvikmath/nlp-recipe-project
https://github.com/rlaengud123/CMC_LRCN
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://github.com/rlax59us/MT3-MAESTRO-pytorch
https://github.com/rmit-ir/RBU
An Axiomatic Analysis of Diversity Evaluation Metrics: Introducing the Rank-Biased Utility Metric
https://github.com/rmit-ir/fair-arguments
https://github.com/rn123/japanese_text_analysis
Finding Structure in Text, Genome and Other Symbolic Sequences
https://github.com/robot-learning-freiburg/DynaFill
Dynamic Object Removal and Spatio-Temporal RGB-D Inpainting via Geometry-Aware Adversarial Learning
https://github.com/robotics-4-all/ISSEL-Announcements
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
https://github.com/rodgzilla/NVSM_pytorch
https://github.com/rohitbhaskar/online-ads-repository
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/ronghanghu/natural-language-object-retrieval
https://github.com/roudimit/AVLnet
HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
https://github.com/royjames/room-impulse-responses
dEchorate: a Calibrated Room Impulse Response Database for Echo-aware Signal Processing
https://github.com/ruanchaves/BLINK
Scalable Zero-shot Entity Linking with Dense Entity Retrieval
https://github.com/rui-yan/CS229-final-project
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.