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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 4576–4600 of 9051 papers

TitleStatusHype
DeepGen: Diverse Search Ad Generation and Real-Time Customization—0
Deep Generative Inpainting with Comparative Sample Augmentation—0
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models—0
Deep Generative Modelling of Human Reach-and-Place Action—0
Deep Generative Models: Deterministic Prediction with an Application in Inverse Rendering—0
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
Deep Learning for Asynchronous Massive Access with Data Frame Length Diversity—0
Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends—0
Deep Learning for Ultrasound Speed-of-Sound Reconstruction: Impacts of Training Data Diversity on Stability and Robustness—0
Deep Learning of Determinantal Point Processes via Proper Spectral Sub-gradient—0
Deep learning on butterfly phenotypes tests evolution's oldest mathematical model—0
Deep Modularity Networks with Diversity--Preserving Regularization—0
Deep Negative Correlation Classification—0
Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography—0
Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness—0
DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets—0
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