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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 981990 of 9051 papers

TitleStatusHype
Adaptable Agent Populations via a Generative Model of PoliciesCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Multimodal Multi-objective Optimization: Comparative Study of the State-of-the-ArtCode1
Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language ModelsCode1
Multi-Objective Counterfactual ExplanationsCode1
Multi-Objective Evolutionary Design of Composite Data-Driven ModelsCode1
Augmenting Sequential Recommendation with Balanced Relevance and DiversityCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
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