https://github.com/ericlee878/ScienceDeJargonizer
공식 코드De-jargonizing Science for Journalists with GPT-4: A Pilot Study
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/ethan00si/seater_generative_retrieval
공식 코드Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning
https://github.com/ethancaballero/broken_neural_scaling_laws
공식 코드https://github.com/ethannlin/schnovel
공식 코드Evaluating and Enhancing Large Language Models for Novelty Assessment in Scholarly Publications
https://github.com/ethz-asl/hf_net
공식 코드From Coarse to Fine: Robust Hierarchical Localization at Large Scale
https://github.com/ethz-asl/hfnet
공식 코드From Coarse to Fine: Robust Hierarchical Localization at Large Scale
https://github.com/ethz-asl/lcd
공식 코드LCD -- Line Clustering and Description for Place Recognition
https://github.com/ethz-asl/segmap
공식 코드SegMap: Segment-based mapping and localization using data-driven descriptors
https://github.com/eugene-yang/lsr-for-tar
공식 코드https://github.com/eugene-yang/tar-content-moderation
공식 코드TAR on Social Media: A Framework for Online Content Moderation
https://github.com/eugene-yang/tarexp
공식 코드TARexp: A Python Framework for Technology-Assisted Review Experiments
https://github.com/eunseongc/spade
공식 코드SpaDE: Improving Sparse Representations using a Dual Document Encoder for First-stage Retrieval
https://github.com/eunyi-lyou/MA-ZS-SBIR
공식 코드Modality-Aware Representation Learning for Zero-shot Sketch-based Image Retrieval
https://github.com/eustema-s-p-a/scarf
공식 코드https://github.com/evanmy/voxel_shape_analysis
공식 코드A Convolutional Autoencoder Approach to Learn Volumetric Shape Representations for Brain Structures
https://github.com/everm0re/erarag-official
공식 코드EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora
https://github.com/exe1023/DialEvalMetrics
공식 코드https://github.com/exiled1143/retrieval-augmented-language-model-for-multi-label-knowledge-graph-link-prediction
공식 코드Retrieval-Augmented Language Model for Extreme Multi-Label Knowledge Graph Link Prediction
https://github.com/exoml/plan-net
공식 코드An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
https://github.com/expectopatronum/mml2020-experiments
공식 코드https://github.com/expertailab/textual-entailment-for-effective-triple-validation-in-object-prediction
공식 코드Textual Entailment for Effective Triple Validation in Object Prediction
https://github.com/explainableml/egocvr
공식 코드EgoCVR: An Egocentric Benchmark for Fine-Grained Composed Video Retrieval
https://github.com/explainableml/flair
공식 코드FLAIR: VLM with Fine-grained Language-informed Image Representations
https://github.com/explainableml/probvlm
공식 코드ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models
https://github.com/explainableml/sketch-primitives
공식 코드https://github.com/explainableml/vision_by_language
공식 코드Vision-by-Language for Training-Free Compositional Image Retrieval
https://github.com/explainableml/waffleclip
공식 코드Waffling around for Performance: Visual Classification with Random Words and Broad Concepts
https://github.com/exploration-lab/iitk-semeval-2024-task-2-clinical-nli
공식 코드https://github.com/exploration-lab/il-pcr
공식 코드U-CREAT: Unsupervised Case Retrieval using Events extrAcTion
https://github.com/exponentiai/arnet
공식 코드ARNet: Self-Supervised FG-SBIR with Unified Sample Feature Alignment and Multi-Scale Token Recycling
https://github.com/exponentiai/effnet
공식 코드ARNet: Self-Supervised FG-SBIR with Unified Sample Feature Alignment and Multi-Scale Token Recycling
https://github.com/f90/jamendolyrics
공식 코드End-to-end Lyrics Alignment for Polyphonic Music Using an Audio-to-Character Recognition Model
https://github.com/facebookresearch/BLINK
공식 코드Scalable Zero-shot Entity Linking with Dense Entity Retrieval
https://github.com/facebookresearch/DPR
공식 코드https://github.com/facebookresearch/DrQA
공식 코드https://github.com/facebookresearch/FiD
공식 코드Distilling Knowledge from Reader to Retriever for Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.