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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 22612270 of 9051 papers

TitleStatusHype
GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial NetworksCode0
Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data CollectionCode0
Problematic Tokens: Tokenizer Bias in Large Language ModelsCode0
GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture ModelingCode0
CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight SharingCode0
GiantHunter: Accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree searchCode0
Global Counterfactual DirectionsCode0
Camera Style Adaptation for Person Re-identificationCode0
An Open-World, Diverse, Cross-Spatial-Temporal Benchmark for Dynamic Wild Person Re-IdentificationCode0
GFlowNets and variational inferenceCode0
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