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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 27012710 of 9051 papers

TitleStatusHype
An Online Approach to Dynamic Channel Access and Transmission Scheduling0
AnomalyPainter: Vision-Language-Diffusion Synergy for Zero-Shot Realistic and Diverse Industrial Anomaly Synthesis0
Calibrated Vehicle Paint Signatures for Simulating Hyperspectral Imagery0
Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs0
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages0
CAFES: A Collaborative Multi-Agent Framework for Multi-Granular Multimodal Essay Scoring0
CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling0
AnomalyFactory: Regard Anomaly Generation as Unsupervised Anomaly Localization0
CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings0
CAD: Photorealistic 3D Generation via Adversarial Distillation0
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