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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 22512260 of 9051 papers

TitleStatusHype
Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark0
Assessment of Left Atrium Motion Deformation Through Full Cardiac Cycle0
RAGSys: Item-Cold-Start Recommender as RAG System0
Hunting for Polluted White Dwarfs and Other Treasures with Gaia XP Spectra and Unsupervised Machine Learning0
Multilingual Diversity Improves Vision-Language Representations0
GenWarp: Single Image to Novel Views with Semantic-Preserving Generative WarpingCode3
GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy PredictionCode4
Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling0
Benchmarking General-Purpose In-Context Learning0
Aperture Selection for CAP Arrays (CAPAs)0
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