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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 4476–4500 of 9051 papers

TitleStatusHype
DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch—0
Boosting High Resolution Image Classification with Scaling-up TransformersCode0
Beauty beacon: correlated strategies for the Fisher runaway process—0
Convolutional autoencoder-based multimodal one-class classification—0
Exploring Robot Morphology Spaces through Breadth-First Search and Random Query—0
Speed Co-Augmentation for Unsupervised Audio-Visual Pre-training—0
Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience—0
Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug DesignCode0
Generalized Dice Focal Loss trained 3D Residual UNet for Automated Lesion Segmentation in Whole-Body FDG PET/CT ImagesCode0
DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics—0
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning—0
Domain-Guided Conditional Diffusion Model for Unsupervised Domain Adaptation—0
Diversifying Question Generation over Knowledge Base via External Natural Questions—0
M^3CS: Multi-Target Masked Point Modeling with Learnable Codebook and Siamese Decoders—0
A mirror-Unet architecture for PET/CT lesion segmentationCode0
TrTr: A Versatile Pre-Trained Large Traffic Model based on Transformer for Capturing Trajectory Diversity in Vehicle Population—0
AntiBARTy Diffusion for Property Guided Antibody Design—0
Learning to Diversify Neural Text Generation via Degenerative Model—0
American Family Cohort, a data resource description—0
DimCL: Dimensional Contrastive Learning For Improving Self-Supervised Learning—0
Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective—0
Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images—0
PPD: A New Valet Parking Pedestrian Fisheye Dataset for Autonomous Driving—0
Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition—0
Leveraging Diversity in Online Interactions—0
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