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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 46114620 of 9051 papers

TitleStatusHype
A Comparative Study of Question Answering over Knowledge BasesCode0
Local Magnification for Data and Feature Augmentation0
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials DataCode0
User-Specific Bicluster-based Collaborative Filtering: Handling Preference Locality, Sparsity and Subjectivity0
Quality-diversity in dissimilarity spaces0
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating AttentionCode0
Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk DecodingCode1
Arbitrary Style Guidance for Enhanced Diffusion-Based Text-to-Image Generation0
Learning to Model Multimodal Semantic Alignment for Story Visualization0
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent SpacesCode2
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