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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 23812390 of 9051 papers

TitleStatusHype
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequencesCode0
Generating Diverse and Meaningful CaptionsCode0
Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture SynthesisCode0
An Evaluation Framework for Attributed Information Retrieval using Large Language ModelsCode0
Generating Diverse and Accurate Visual Captions by Comparative Adversarial LearningCode0
Toward Improving Coherence and Diversity of Slogan GenerationCode0
Generating Diverse and High-Quality Texts by Minimum Bayes Risk DecodingCode0
Generating Informative and Diverse Conversational Responses via Adversarial Information MaximizationCode0
A Neural Compositional Paradigm for Image CaptioningCode0
Generate then Refine: Data Augmentation for Zero-shot Intent DetectionCode0
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