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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 49014910 of 9051 papers

TitleStatusHype
Exploiting Feature Diversity for Make-up Temporal Video Grounding0
Diverse Generative Perturbations on Attention Space for Transferable Adversarial AttacksCode1
Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery0
Reducing Exploitability with Population Based TrainingCode0
Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning0
Adaptive Learning Rates for Faster Stochastic Gradient Methods0
r/K selection of GC content in prokaryotes0
Improving COVID-19 CT Classification of CNNs by Learning Parameter-Efficient Representation0
Deep Billboards towards Lossless Real2Sim in Virtual Reality0
A Map of Diverse Synthetic Stable Roommates InstancesCode1
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