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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 3981–3990 of 9051 papers

TitleStatusHype
Generating Multiple Diverse Responses for Short-Text Conversation—0
Cuid: A new study of perceived image quality and its subjective assessment—0
Hierarchical Modes Exploring in Generative Adversarial Networks—0
Hierarchical protein backbone generation with latent and structure diffusion—0
Diverse Imagenet Models Transfer Better—0
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network—0
Diverse Image Inpainting with Bidirectional and Autoregressive Transformers—0
Generating Responses with a Specific Emotion in Dialog—0
Informed Sampling for Diversity in Concept-to-Text NLG—0
Block-Wise MAP Inference for Determinantal Point Processes with Application to Change-Point Detection—0
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