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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 2081–2090 of 9051 papers

TitleStatusHype
LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images—0
Large Language Model as a Universal Clinical Multi-task Decoder—0
Can Go AIs be adversarially robust?Code2
Insect Identification in the Wild: The AMI DatasetCode0
GameVibe: A Multimodal Affective Game Corpus—0
Fast uncovering of protein sequence diversity from structure—0
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision—0
Decomposed evaluations of geographic disparities in text-to-image models—0
Skip-Layer Attention: Bridging Abstract and Detailed Dependencies in Transformers—0
When Box Meets Graph Neural Network in Tag-aware RecommendationCode0
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