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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 881890 of 9051 papers

TitleStatusHype
Aligning Language Models with Preferences through f-divergence MinimizationCode1
NL2CMD: An Updated Workflow for Natural Language to Bash Commands TranslationCode1
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial ExamplesCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal AnchorsCode1
Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using SamplesCode1
Towards Geospatial Foundation Models via Continual PretrainingCode1
Sample-efficient Multi-objective Molecular Optimization with GFlowNetsCode1
MMPD: Multi-Domain Mobile Video Physiology DatasetCode1
Mask Conditional Synthetic Satellite ImageryCode1
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