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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 1501–1510 of 9051 papers

TitleStatusHype
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse DesignsCode1
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse GranularityCode1
Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence LearningCode1
On the effectiveness of partial variance reduction in federated learning with heterogeneous dataCode1
GLAMOUR: Graph Learning over Macromolecule RepresentationsCode1
Local Patch AutoAugment with Multi-Agent CollaborationCode1
Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRICode1
PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse DesignCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
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