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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 26412650 of 9051 papers

TitleStatusHype
Progressive trajectory matching for medical dataset distillation0
On Pretraining Data Diversity for Self-Supervised LearningCode1
Wav2Gloss: Generating Interlinear Glossed Text from SpeechCode0
Diversity-Aware Agnostic Ensemble of Sharpness Minimizers0
Planner3D: LLM-enhanced graph prior meets 3D indoor scene explicit regularization0
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial TrajectoryCode1
DreamDA: Generative Data Augmentation with Diffusion ModelsCode1
Controllable Face Synthesis with Semantic Latent Diffusion ModelsCode1
Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal DataCode0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making0
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