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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 4501–4525 of 9051 papers

TitleStatusHype
Neural Story Planning—0
Scaffold-Based Multi-Objective Drug Candidate Optimization—0
Detecting Bone Lesions in X-Ray Under Diverse Acquisition Conditions—0
Objaverse: A Universe of Annotated 3D Objects—0
Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation—0
Calibration-Free Driver Drowsiness Classification based on Manifold-Level AugmentationCode0
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
APOLLO: An Optimized Training Approach for Long-form Numerical ReasoningCode1
Deep Negative Correlation Classification—0
Multiple Phase Transitions Shape Biodiversity of a Migrating Population—0
RT-1: Robotics Transformer for Real-World Control at ScaleCode3
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection—0
A Data Quality Assessment Framework for AI-enabled Wireless Communication—0
Evaluation of Synthetic Datasets for Conversational Recommender Systems—0
BigText-QA: Question Answering over a Large-Scale Hybrid Knowledge Graph—0
Decomposition of the Leinster-Cobbold Diversity Index—0
Cap2Aug: Caption guided Image to Image data Augmentation—0
Invasion and Interaction Determine Population Composition in an Open Evolving System—0
Progressive Multi-view Human Mesh Recovery with Self-Supervision—0
Going beyond richness: Modelling the BEF relationship using species identity, evenness, richness and species interactions via the DImodels R package—0
Frugal Reinforcement-based Active Learning—0
Effective Dynamics of Generative Adversarial Networks—0
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection—0
MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis—0
DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue DatasetCode1
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