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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 4311–4320 of 9051 papers

TitleStatusHype
Learning from Multiple Sources for Data-to-Text and Text-to-DataCode0
Feasible Recourse Plan via Diverse InterpolationCode0
Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship—0
Deep Active Learning in the Presence of Label Noise: A Survey—0
Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles—0
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management—0
Texturize a GAN Using a Single Image—0
Mental Health Coping Stories on Social Media: A Causal-Inference Study of Papageno Effect—0
Learning temporal relationships between symbols with Laplace Neural Manifolds—0
Metropolis Theorem and Its Applications in Single Image Detail EnhancementCode0
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