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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 48514860 of 9051 papers

TitleStatusHype
Probing Contextual Diversity for Dense Out-of-Distribution DetectionCode0
ASpanFormer: Detector-Free Image Matching with Adaptive Span TransformerCode2
Video-based Cross-modal Auxiliary Network for Multimodal Sentiment AnalysisCode0
Understanding Diversity in Session-Based RecommendationCode0
An Energy Activity Dataset for Smart Homes0
Chosen methods of improving small object recognition with weak recognizable features0
Grounded Affordance from Exocentric ViewCode1
ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers0
A Diversity-Aware Domain Development Methodology0
Tensor Decomposition based Personalized Federated Learning0
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