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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 46514660 of 9051 papers

TitleStatusHype
Designing a Robust Radiology Report Generation System0
Designing Data: Proactive Data Collection and Iteration for Machine Learning0
Designing Recommender Systems to Depolarize0
Market Design with Distributional Objectives0
DesnowNet: Context-Aware Deep Network for Snow Removal0
Detail-Preserving Latent Diffusion for Stable Shadow Removal0
Detecting Bone Lesions in X-Ray Under Diverse Acquisition Conditions0
Detecting patterns of species diversification in the presence of both rate shifts and mass extinctions0
Detecting Quality Problems in Data Models by Clustering Heterogeneous Data Values0
Detecting Sockpuppets in Deceptive Opinion Spam0
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