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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 46014610 of 9051 papers

TitleStatusHype
DeepPrivacy2: Towards Realistic Full-Body AnonymizationCode2
Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation0
Towards Building Text-To-Speech Systems for the Next Billion UsersCode2
Super-resolution Reconstruction of Single Image for Latent features0
Availability, outage, and capacity of spatially correlated, Australasian free-space optical networks0
GAMMT: Generative Ambiguity Modeling Using Multiple Transformers0
Holographic Integrated Sensing and Communications: Principles, Technology, and Implementation0
Person Text-Image Matching via Text-Feature Interpretability Embedding and External Attack Node ImplantationCode0
Diversity and bias in audio captioning datasets0
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision ResearchCode1
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