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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 21612170 of 9051 papers

TitleStatusHype
Tree balance in phylogenetic models0
URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement0
Language Guided Skill Discovery0
Boosting Diffusion Model for Spectrogram Up-sampling in Text-to-speech: An Empirical Study0
Retrieval & Fine-Tuning for In-Context Tabular Models0
CTSyn: A Foundational Model for Cross Tabular Data Generation0
HateDebias: On the Diversity and Variability of Hate Speech Debiasing0
The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed TomographyCode1
DiNeR: a Large Realistic Dataset for Evaluating Compositional GeneralizationCode0
CLoG: Benchmarking Continual Learning of Image Generation ModelsCode1
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