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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 54215430 of 9051 papers

TitleStatusHype
Steering Language Generation: Harnessing Contrastive Expert Guidance and Negative Prompting for Coherent and Diverse Synthetic Data Generation0
Steering Responsible AI: A Case for Algorithmic Pluralism0
Steganalysis of Image with Adaptively Parametric Activation0
Stigmergy-based collision-avoidance algorithm for self-organising swarms0
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning0
STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting0
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation0
Stochastic Channel Decorrelation Network and Its Application to Visual Tracking0
Stochastic Conditional Generative Networks with Basis Decomposition0
Stochastic Linear Contextual Bandits with Diverse Contexts0
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