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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 521530 of 9051 papers

TitleStatusHype
Differential Evolution with Reversible Linear TransformationsCode1
BanglaParaphrase: A High-Quality Bangla Paraphrase DatasetCode1
Barbie: Text to Barbie-Style 3D AvatarsCode1
Barcode Method for Generative Model Evaluation driven by Topological Data AnalysisCode1
Adversarial Semantic Data Augmentation for Human Pose EstimationCode1
Generating Diverse High-Fidelity Images with VQ-VAE-2Code1
Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text GenerationCode1
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
Difficulty-Aware Simulator for Open Set RecognitionCode1
Bacteriophage classification for assembled contigs using Graph Convolutional NetworkCode1
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