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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 511520 of 9051 papers

TitleStatusHype
Barcode Method for Generative Model Evaluation driven by Topological Data AnalysisCode1
Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text GenerationCode1
Bayesian Adversarial Human Motion SynthesisCode1
DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue DatasetCode1
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Diffusion ModelCode1
Unconstrained Face-Mask & Face-Hand Datasets: Building a Computer Vision System to Help Prevent the Transmission of COVID-19Code1
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label EnhancementCode1
Bacteriophage classification for assembled contigs using Graph Convolutional NetworkCode1
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal TransferCode1
DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose EstimationCode1
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