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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 41414150 of 9051 papers

TitleStatusHype
Text-Conditional Contextualized Avatars For Zero-Shot Personalization0
The Second Monocular Depth Estimation Challenge0
Learning to exploit z-Spatial Diversity for Coherent Nonlinear Optical Fiber Communication0
SiLK -- Simple Learned KeypointsCode2
Mutation enhances cooperation in direct reciprocity0
LINGO : Visually Debiasing Natural Language Instructions to Support Task Diversity0
Towards Large-Scale Simulations of Open-Ended Evolution in Continuous Cellular Automata0
NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANsCode1
Wild Face Anti-Spoofing Challenge 2023: Benchmark and ResultsCode0
Probabilistic Human Mesh Recovery in 3D Scenes from Egocentric ViewsCode1
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