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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 38413850 of 9051 papers

TitleStatusHype
Diverse mini-batch Active Learning0
Full-band General Audio Synthesis with Score-based Diffusion0
A Tutorial On Intersectionality in Fair Rankings0
Attribution for Enhanced Explanation with Transferable Adversarial eXploration0
Diverse Melody Generation from Chinese Lyrics via Mutual Information Maximization0
CPLM, a Parallel Corpus for Mexican Languages: Development and Interface0
BOOST: Bootstrapping Strategy-Driven Reasoning Programs for Program-Guided Fact-Checking0
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles0
Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching0
Diverse Machine Translation with a Single Multinomial Latent Variable0
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