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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 35213530 of 9051 papers

TitleStatusHype
PPD: A New Valet Parking Pedestrian Fisheye Dataset for Autonomous Driving0
Leveraging Diversity in Online Interactions0
Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition0
Design of Chain-of-Thought in Math Problem SolvingCode1
NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource LanguagesCode1
Motif-Centric Representation Learning for Symbolic MusicCode0
NSOAMT -- New Search Only Approach to Machine Translation0
ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency DistillationCode1
GDM: Dual Mixup for Graph Classification with Limited Supervision0
Grasp-Anything: Large-scale Grasp Dataset from Foundation ModelsCode2
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