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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 39413950 of 9051 papers

TitleStatusHype
NashFormer: Leveraging Local Nash Equilibria for Semantically Diverse Trajectory Prediction0
Whitening-based Contrastive Learning of Sentence EmbeddingsCode1
Just a Glimpse: Rethinking Temporal Information for Video Continual Learning0
The use of Ethnomedicinal plants in Indigenous Health Care Practice of the Hajong Tribe community in Durgapur, Bangladesh0
Rethinking Masked Language Modeling for Chinese Spelling CorrectionCode1
Stochastic Pitch Prediction Improves the Diversity and Naturalness of Speech in Glow-TTSCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Exploiting Abstract Meaning Representation for Open-Domain Question AnsweringCode1
Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results0
Benchmarking state-of-the-art gradient boosting algorithms for classification0
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