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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 931940 of 9051 papers

TitleStatusHype
Enhancing Annotated Bibliography Generation with LLM Ensembles0
Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive DefenseCode1
The Synergy of Automated Pipelines with Prompt Engineering and Generative AI in Web Crawling0
Revealing the Shape of Genome Space via K-mer TopologyCode0
No Preference Left Behind: Group Distributional Preference OptimizationCode1
Multi-scale Latent Point Consistency Models for 3D Shape Generation0
Focusing Image Generation to Mitigate Spurious Correlations0
Diverse Rare Sample Generation with Pretrained GANsCode0
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task SynthesisCode3
Learning states enhanced knowledge tracing: Simulating the diversity in real-world learning process0
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