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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 29412950 of 9051 papers

TitleStatusHype
Expanding functional protein sequence space using generative adversarial networksCode0
Deconditional Downscaling with Gaussian ProcessesCode0
Evolvability ES: Scalable and Direct Optimization of EvolvabilityCode0
Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context LearningCode0
Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity AlgorithmCode0
Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible TemplatesCode0
Exploring Diversity in Back Translation for Low-Resource Machine TranslationCode0
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
Decomposed Distribution Matching in Dataset CondensationCode0
Event Transition Planning for Open-ended Text GenerationCode0
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