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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 761770 of 9051 papers

TitleStatusHype
Towards Viewpoint-Invariant Visual Recognition via Adversarial TrainingCode1
Generative Meta-Learning Robust Quality-Diversity PortfolioCode1
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image ModelsCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Answering Ambiguous Questions via Iterative PromptingCode1
VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel SynthesisCode1
VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning BenchmarksCode1
Monte Carlo Policy Gradient Method for Binary OptimizationCode1
Generative Data Augmentation for Aspect Sentiment Quad PredictionCode1
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural NetworksCode1
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