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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 851860 of 9051 papers

TitleStatusHype
2D medical image synthesis using transformer-based denoising diffusion probabilistic modelCode1
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable SimulationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Implicit Neural Representations for Variable Length Human Motion GenerationCode1
AugMax: Adversarial Composition of Random Augmentations for Robust TrainingCode1
Vision Transformers with Patch DiversificationCode1
Improving Adversarial Transferability with Gradient RefiningCode1
A Sentence Cloze Dataset for Chinese Machine Reading ComprehensionCode1
Active Learning by Acquiring Contrastive ExamplesCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
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