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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

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Showing 3551–3560 of 9051 papers

TitleStatusHype
An Inclusive Foundation Model for Generalizable Cytogenetics in Precision Oncology—0
Assessing Intra-class Diversity and Quality of Synthetically Generated Images in a Biomedical and Non-biomedical Setting—0
Facilitating Opinion Diversity through Hybrid NLP Approaches—0
Factor-Conditioned Speaking-Style Captioning—0
Consecutive Question Generation with Multitask Joint Reranking and Dynamic Rationale Search—0
Factorised Speaker-environment Adaptive Training of Conformer Speech Recognition Systems—0
Factorized Transformer for Multi-Domain Neural Machine Translation—0
Facts are Harder Than Opinions -- A Multilingual, Comparative Analysis of LLM-Based Fact-Checking Reliability—0
Considerations for Ethical Speech Recognition Datasets—0
Diversified Multiscale Graph Learning with Graph Self-Correction—0
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