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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

Showing 881890 of 9051 papers

TitleStatusHype
Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoECode1
Jointly Measuring Diversity and Quality in Text Generation ModelsCode1
Keiki: Towards Realistic Danmaku Generation via Sequential GANsCode1
KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse TransformationCode1
Key-Exchange Convolutional Auto-Encoder for Data Augmentation in Early Knee Osteoarthritis DetectionCode1
Keyphrase Generation with Cross-Document AttentionCode1
Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision ModelsCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
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