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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 40014010 of 9051 papers

TitleStatusHype
Diversity and Inclusion in Artificial Intelligence0
Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies0
Enhancing biodiversity through intraspecific suppression in large ecosystems0
Multi-factor Sequential Re-ranking with Perception-Aware Diversification0
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data0
CoMusion: Towards Consistent Stochastic Human Motion Prediction via Motion DiffusionCode1
MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph FusionCode0
On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training0
Modeling the Q-Diversity in a Min-max Play Game for Robust OptimizationCode0
Boosting Human-Object Interaction Detection with Text-to-Image Diffusion ModelCode1
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