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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 7776–7800 of 14297 papers

TitleStatusHype
Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event SequencesCode0
Deep Iterative Phase Retrieval for Ptychography—0
Retrieval-Augmented Reinforcement Learning—0
Improving Rating and Relevance with Point-of-Interest Recommender System—0
Entropic Associative Memory for Manuscript SymbolsCode0
Question-Answer Sentence Graph for Joint Modeling Answer Selection—0
PCRP: Unsupervised Point Cloud Object Retrieval and Pose Estimation—0
ITTC @ TREC 2021 Clinical Trials Track—0
Efficient Cross-Modal Retrieval via Deep Binary Hashing and QuantizationCode0
Low-Rank Phase Retrieval with Structured Tensor Models—0
Saving Dense Retriever from Shortcut Dependency in Conversational SearchCode1
Situation-based memory in spiking neuron-astrocyte network—0
CommerceMM: Large-Scale Commerce MultiModal Representation Learning with Omni Retrieval—0
Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation—0
Case law retrieval: problems, methods, challenges and evaluations in the last 20 years—0
Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation—0
Do Lessons from Metric Learning Generalize to Image-Caption Retrieval?Code0
Matching Tweets With Applicable Fact-Checks Across Languages—0
vue4logs -- Automatic Structuring of Heterogeneous Computer System Logs—0
Transformer Memory as a Differentiable Search IndexCode1
DS4DH at TREC Health Misinformation 2021: Multi-Dimensional Ranking Models with Transfer Learning and Rank Fusion—0
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training BenchmarkCode0
Tightly Coupled Learning Strategy for Weakly Supervised Hierarchical Place Recognition—0
On the Importance of Building High-quality Training Datasets for Neural Code SearchCode1
Probabilistic Embeddings RevisitedCode1
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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