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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 4551–4575 of 9051 papers

TitleStatusHype
Decomposed evaluations of geographic disparities in text-to-image models—0
Decomposition of the Leinster-Cobbold Diversity Index—0
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel—0
Decoupling Pragmatics: Discriminative Decoding for Referring Expression Generation—0
Decoupling Shape and Density for Liver Lesion Synthesis Using Conditional Generative Adversarial Networks—0
Deep Active Learning for Multi-Label Classification of Remote Sensing Images—0
Does Deep Active Learning Work in the Wild?—0
Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient—0
Deep Active Learning for Text Classification with Diverse Interpretations—0
Deep Active Learning in the Open World—0
Deep Active Learning in the Presence of Label Noise: A Survey—0
Deep Architectures and Ensembles for Semantic Video Classification—0
Deep Billboards towards Lossless Real2Sim in Virtual Reality—0
Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Images—0
Deep Concept Identification for Generative Design—0
Deep Convolutional Compression for Massive MIMO CSI Feedback—0
Deep Delay Loop Reservoir Computing for Specific Emitter Identification—0
Deep Determinantal Point Processes—0
Deep Dynamic Neural Network to trade-off between Accuracy and Diversity in a News Recommender System—0
Deep Ensemble Collaborative Learning by using Knowledge-transfer Graph for Fine-grained Object Classification—0
Deep Ensemble Policy Learning—0
Deep Ensembles for Low-Data Transfer Learning—0
Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19—0
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks—0
Deep Gait Tracking With Inertial Measurement Unit—0
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