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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 361370 of 9051 papers

TitleStatusHype
A Large-Scale Study on Video Action Dataset CondensationCode1
Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive DefenseCode1
No Preference Left Behind: Group Distributional Preference OptimizationCode1
Improving Integrated Gradient-based Transferable Adversarial Examples by Refining the Integration PathCode1
Optimal signal transmission and timescale diversity in a model of human brain operating near criticalityCode1
HSEvo: Elevating Automatic Heuristic Design with Diversity-Driven Harmony Search and Genetic Algorithm Using LLMsCode1
Hybrid CNN-LSTM based Indoor Pedestrian Localization with CSI Fingerprint MapsCode1
StrandHead: Text to Strand-Disentangled 3D Head Avatars Using Hair Geometric PriorsCode1
Relation-Guided Adversarial Learning for Data-free Knowledge TransferCode1
Exploring Semantic Consistency and Style Diversity for Domain Generalized Semantic SegmentationCode1
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