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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 961970 of 9051 papers

TitleStatusHype
Diversity is All You Need: Learning Skills without a Reward FunctionCode1
Diversity-Measurable Anomaly DetectionCode1
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active ExplorationCode1
Modality Plug-and-Play: Elastic Modality Adaptation in Multimodal LLMs for Embodied AICode1
Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word ExclusionCode1
Modeling Thousands of Human Annotators for Generalizable Text-to-Image Person Re-identificationCode1
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading BooksCode1
Fully Unsupervised Diversity Denoising with Convolutional Variational AutoencodersCode1
Exploring Effective Data for Surrogate Training Towards Black-Box AttackCode1
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
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