SOTAVerified

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 371380 of 9051 papers

TitleStatusHype
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
Automating Rigid Origami DesignCode1
Data Augmentation using Pre-trained Transformer ModelsCode1
Dataset Factorization for CondensationCode1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
Deep Image Harmonization with Learnable AugmentationCode1
Dan: Deep attention neural network for news recommendationCode1
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
Action detection using a neural network elucidates the genetics of mouse grooming behaviorCode1
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