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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

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Showing 3421–3430 of 9051 papers

TitleStatusHype
Mitigating stereotypical biases in text to image generative systems—0
Score-Based Generative Models for Designing Binding Peptide BackbonesCode1
Adversarial optimization leads to over-optimistic security-constrained dispatch, but sampling can help—0
Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models—0
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural FeaturesCode0
Hexa: Self-Improving for Knowledge-Grounded Dialogue System—0
CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental SegmentationCode1
SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQACode0
Diversity from Human Feedback—0
Growing ecosystem of deep learning methods for modeling proteinx2013protein interactions—0
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