https://github.com/dmis-lab/covidsearch
Contextualized Sparse Representations for Real-Time Open-Domain Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Contextualized Sparse Representations for Real-Time Open-Domain Question Answering
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing
DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Repeatability Is Not Enough: Learning Affine Regions via Discriminability
Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations
Revealing the Importance of Semantic Retrieval for Machine Reading at Scale
FMHash: Deep Hashing of In-Air-Handwriting for User Identification
Contrastive Learning of Medical Visual Representations from Paired Images and Text
Contrastive Learning of Medical Visual Representations from Paired Images and Text
ATR4S: Toolkit with State-of-the-art Automatic Terms Recognition Methods in Scala
Learning Visual Importance for Graphic Designs and Data Visualizations
Reproducible scaling laws for contrastive language-image learning
ConQRet: Benchmarking Fine-Grained Evaluation of Retrieval Augmented Argumentation with LLM Judges
RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models
Working hard to know your neighbor's margins: Local descriptor learning loss
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.