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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 4611–4620 of 9051 papers

TitleStatusHype
Deep Reinforcement Learning with Hybrid Intrinsic Reward Model—0
Deep Reinforcement Learning with Quantum-inspired Experience Replay—0
DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement—0
Deep Representation Learning for Multi-functional Degradation Modeling of Community-dwelling Aging Population—0
Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective—0
Deep Sky Modeling for Single Image Outdoor Lighting Estimation—0
Deep soccer captioning with transformer: dataset, semantics-related losses, and multi-level evaluation—0
Deep Submodular Networks for Extractive Data Summarization—0
Deep Surrogate Assisted Generation of Environments—0
Deep Unsupervised Identification of Selected SNPs between Adapted Populations on Pool-seq Data—0
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