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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 37613770 of 9051 papers

TitleStatusHype
Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax0
Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium PhysicsCode0
Leveraging Contextual Counterfactuals Toward Belief Calibration0
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image ModelsCode1
Self-regulating Prompts: Foundational Model Adaptation without ForgettingCode2
Neutral Diversity in Experimental Metapopulations0
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation0
Rethinking Mitosis Detection: Towards Diverse Data and Feature RepresentationCode0
Neural Machine Translation Data Generation and Augmentation using ChatGPT0
Objaverse-XL: A Universe of 10M+ 3D ObjectsCode3
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