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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 37713780 of 9051 papers

TitleStatusHype
Objaverse-XL: A Universe of 10M+ 3D ObjectsCode3
Neural Machine Translation Data Generation and Augmentation using ChatGPT0
Entity Identifier: A Natural Text Parsing-based Framework For Entity Relation Extraction0
Fatal errors and misuse of mathematics in the Hong-Page Theorem and Landemore's epistemic argument0
Fairness and Diversity in Recommender Systems: A SurveyCode0
Measuring Lexical Diversity in Texts: The Twofold Length Problem0
SVIT: Scaling up Visual Instruction TuningCode3
Answering Ambiguous Questions via Iterative PromptingCode1
AI and the EU Digital Markets Act: Addressing the Risks of Bigness in Generative AI0
VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel SynthesisCode1
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