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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 51615170 of 9051 papers

TitleStatusHype
SDTP: Semantic-aware Decoupled Transformer Pyramid for Dense Image Prediction0
SEAL: Semantic Attention Learning for Long Video Representation0
Search For Deep Graph Neural Networks0
Searching for Designs in-between0
Search results diversification in competitive search0
Seasonal Station-Keeping of Short Duration High Altitude Balloons using Deep Reinforcement Learning0
Sea You Later: Metadata-Guided Long-Term Re-Identification for UAV-Based Multi-Object Tracking0
Secrecy Performance of Antenna-Selection-Aided MIMOME Channels with BPSK/QPSK Modulations0
Secure and Privacy Preserving Proxy Biometrics Identities0
Secure Power Control for Downlink Cell-Free Massive MIMO With Passive Eavesdroppers0
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