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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 411420 of 9051 papers

TitleStatusHype
Gradient-based Sample Selection for Faster Bayesian Optimization0
ID-Booth: Identity-consistent Face Generation with Diffusion ModelsCode1
DiverseFlow: Sample-Efficient Diverse Mode Coverage in Flows0
PinRec: Outcome-Conditioned, Multi-Token Generative Retrieval for Industry-Scale Recommendation Systems0
More diverse more adaptive: Comprehensive Multi-task Learning for Improved LLM Domain Adaptation in E-commerce0
MDIT: A Model-free Data Interpolation Method for Diverse Instruction Tuning0
MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular Detection0
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation0
On the Dynamics of Mating Preferences in Genetic Programming0
CamContextI2V: Context-aware Controllable Video GenerationCode1
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