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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 4671–4680 of 9051 papers

TitleStatusHype
DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification—0
DFlow: Diverse Dialogue Flow Simulation with Large Language Models—0
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning—0
DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot Learning—0
DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception—0
DH-Set: Improving Vision-Language Alignment with Diverse and Hybrid Set-Embeddings Learning—0
Dialect Adaptation and Data Augmentation for Low-Resource ASR: TalTech Systems for the MADASR 2023 Challenge—0
Dialect Diversity in Text Summarization on Twitter—0
Dialect-Specific Models for Automatic Speech Recognition of African American Vernacular English—0
Dialect Transfer for Swiss German Speech Translation—0
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