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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 5211–5220 of 9051 papers

TitleStatusHype
Progressive Ensemble Networks for Zero-Shot Recognition—0
Self-Training for End-to-End Speech Recognition—0
Seller-side Outcome Fairness in Online Marketplaces—0
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning—0
Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts—0
Semantic-Aware Representation of Multi-Modal Data for Data Ingress: A Literature Review—0
Semantic Cells: Evolutional Process to Acquire Sense Diversity of Items—0
Semantic Diversity by Phonetics for Accurate and Robust Machine Translation—0
Semantic Diversity for Natural Language Understanding Evaluation in Dialog Systems—0
Semantic Diversity in Dialogue with Natural Language Inference—0
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