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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 15111520 of 9051 papers

TitleStatusHype
Explain Me the Painting: Multi-Topic Knowledgeable Art Description GenerationCode1
Explicit Syntactic Guidance for Neural Text GenerationCode1
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active ExplorationCode1
Class-Aware Mask-Guided Feature Refinement for Scene Text RecognitionCode1
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse GranularityCode1
Score-Based Generative Models for Designing Binding Peptide BackbonesCode1
Class-Balancing Diffusion ModelsCode1
Exploring Empty Spaces: Human-in-the-Loop Data AugmentationCode1
CityPersons: A Diverse Dataset for Pedestrian DetectionCode1
A Map of Diverse Synthetic Stable Roommates InstancesCode1
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