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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 4581–4590 of 9051 papers

TitleStatusHype
Deep Generative Models for 3D Medical Image Synthesis—0
Deep Generative Models for Proton Zero Degree Calorimeter Simulations in ALICE, CERN—0
Deep Hierarchical-Hyperspherical Learning (DH^2L)—0
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection—0
DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection—0
Deep Incomplete Multi-View Multiple Clusterings—0
Deep Internal Learning: Deep Learning from a Single Input—0
Deep Latent-Variable Models for Text Generation—0
Deep Leaning-Based Ultra-Fast Stair Detection—0
Deep Learning-Enabled Zero-Touch Device Identification: Mitigating the Impact of Channel Variability Through MIMO Diversity—0
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