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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 8126–8150 of 9051 papers

TitleStatusHype
ControlVAE: Controllable Variational Autoencoder—0
Adversarial Learning of Semantic Relevance in Text to Image Synthesis—0
Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarios—0
Deep Reinforcement Learning for Inverse Inorganic Materials Design—0
Adversarial Environment Design via Regret-Guided Diffusion Models—0
SVP: Style-Enhanced Vivid Portrait Talking Head Diffusion Model—0
Deep Reinforcement Learning Optimized Intelligent Resource Allocation in Active RIS-Integrated TN-NTN Networks—0
Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversity—0
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization—0
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
Controllable Text Generation with Focused Variation—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
SWAG: A Wrapper Method for Sparse Learning—0
Controllable Satellite-to-Street-View Synthesis with Precise Pose Alignment and Zero-Shot Environmental Control—0
Deep Unsupervised Identification of Selected SNPs between Adapted Populations on Pool-seq Data—0
Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection—0
Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation—0
Defending Active Directory by Combining Neural Network based Dynamic Program and Evolutionary Diversity Optimisation—0
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