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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

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Showing 3191–3200 of 9051 papers

TitleStatusHype
Enhancing Synthetic Oversampling for Imbalanced Datasets Using Proxima-Orion Neighbors and q-Gaussian Weighting Technique—0
Enhancing Targeted Attack Transferability via Diversified Weight Pruning—0
CodeFusion: A Pre-trained Diffusion Model for Code Generation—0
Enhancing Test Time Adaptation with Few-shot Guidance—0
3D Neural Field Generation using Triplane Diffusion—0
Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense—0
Exploring Diverse Expressions for Paraphrase Generation—0
Enhancing the long-term performance of recommender system—0
Diversity-Achieving Slow-DropBlock Network for Person Re-Identification—0
DiversiTree: A New Method to Efficiently Compute Diverse Sets of Near-Optimal Solutions to Mixed-Integer Optimization Problems—0
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