SOTAVerified

Retrieval

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.

Papers

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Showing 2826–2850 of 14297 papers

TitleStatusHype
An Ontology-driven Treatment Article Retrieval System for Precision Oncology—0
Deep Cross-Modal Hashing with Hashing Functions and Unified Hash Codes Jointly Learning—0
Deep Cross-modality Adaptation via Semantics Preserving Adversarial Learning for Sketch-based 3D Shape Retrieval—0
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing—0
A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation—0
Boosting Video-Text Retrieval with Explicit High-Level Semantics—0
Boosting Video Captioning with Dynamic Loss Network—0
Adversarial contamination of networks in the setting of vertex nomination: a new trimming method—0
3D-2D Neural Nets for Phase Retrieval in Noisy Interferometric Imaging—0
Deep Cross-modal Hashing via Margin-dynamic-softmax Loss—0
Deep DNA Storage: Scalable and Robust DNA Storage via Coding Theory and Deep Learning—0
Boosting Text-to-Chart Retrieval through Training with Synthesized Semantic Insights—0
Boosting spatial resolution by incorporating periodic boundary conditions into single-distance hard-x-ray phase retrieval—0
A Noise-Filtering Approach for Cancer Drug Sensitivity Prediction—0
Boosting Search Engines with Interactive Agents—0
A Comparative Analysis of Retrieval Techniques In Content Based Image Retrieval—0
Deep convolutional embedding for digitized painting clustering—0
A Comparative Analysis of Retrievability and PageRank Measures—0
Content-based image retrieval using Mix histogram—0
Boosting Cross-Language Retrieval by Learning Bilingual Phrase Associations from Relevance Rankings—0
Adversarial attacks against Fact Extraction and VERification—0
Deep Contextualized Biomedical Abbreviation Expansion—0
Deep Convolutional Features for Image Based Retrieval and Scene Categorization—0
Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check—0
Boosted Dense Retriever—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second183.53—Unverified
2ElasticsearchQueries per second21.8—Unverified
3BM25-PTQueries per second6.49—Unverified
4Rank-BM25Queries per second1.18—Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second20.88—Unverified
2ElasticsearchQueries per second7.11—Unverified
3Rank-BM25Queries per second0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second41.85—Unverified
2ElasticsearchQueries per second12.16—Unverified
3Rank-BM25Queries per second0.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FLMRRecall@589.32—Unverified
2RA-VQARecall@582.84—Unverified
#ModelMetricClaimedVerifiedStatus
1PreFLMRRecall@562.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP-KIStext-to-video Mean Rank30—Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP4OutfitRecall@57.59—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1—Unverified
#ModelMetricClaimedVerifiedStatus
1COLTCOMP@84.55—Unverified
#ModelMetricClaimedVerifiedStatus
1hello0L1,121,222—Unverified