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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

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Showing 741–750 of 9051 papers

TitleStatusHype
DiTAR: Diffusion Transformer Autoregressive Modeling for Speech Generation—0
From Features to Transformers: Redefining Ranking for Scalable Impact—0
MaxInfo: A Training-Free Key-Frame Selection Method Using Maximum Volume for Enhanced Video Understanding—0
KDA: A Knowledge-Distilled Attacker for Generating Diverse Prompts to Jailbreak LLMs—0
Proportional Selection in Networks—0
Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data—0
Controllable Satellite-to-Street-View Synthesis with Precise Pose Alignment and Zero-Shot Environmental Control—0
How Inclusively do LMs Perceive Social and Moral Norms?Code0
Position Paper: Building Trust in Synthetic Data for Clinical AI—0
Topic Modeling in Marathi—0
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