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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 52315240 of 9051 papers

TitleStatusHype
Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature Aggregation and Pyramid Decoders0
Semantics in Multi-objective Genetic Programming0
Semantic uncertainty guides the extension of conventions to new referents0
Semantic WordRank: Generating Finer Single-Document Summarizations0
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding0
Semi-Implicit Generative Model0
Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models0
Semi-Supervised Convolutional Neural Networks for Human Activity Recognition0
Semi-supervised FusedGAN for Conditional Image Generation0
Semi-Supervised Health Index Monitoring with Feature Generation and Fusion0
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