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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 25812590 of 9051 papers

TitleStatusHype
Carrier Frequency Offset Estimation for OCDM with Null Subchirps0
CARLA Drone: Monocular 3D Object Detection from a Different Perspective0
An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge0
A Comprehensive Augmentation Framework for Anomaly Detection0
DPP-TTS: Diversifying prosodic features of speech via determinantal point processes0
CariGAN: Caricature Generation through Weakly Paired Adversarial Learning0
A novel statistical metric learning for hyperspectral image classification0
A Novel Multiple Interval Prediction Method for Electricity Prices based on Scenarios Generation: Definition and Method0
Capturing the Production of the Innovative Ideas: An Online Social Network Experiment and "Idea Geography" Visualization0
Adversarial Environment Design via Regret-Guided Diffusion Models0
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