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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 451460 of 9051 papers

TitleStatusHype
Synth-Empathy: Towards High-Quality Synthetic Empathy DataCode1
Distributed speech separation in spatially unconstrained microphone arraysCode1
DivAug: Plug-in Automated Data Augmentation with Explicit Diversity MaximizationCode1
DivClust: Controlling Diversity in Deep ClusteringCode1
Diverse and Admissible Trajectory Forecasting through Multimodal Context UnderstandingCode1
Diverse and Admissible Trajectory Prediction through Multimodal Context UnderstandingCode1
AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic DataCode1
Diverse and Specific Clarification Question Generation with KeywordsCode1
Diverse Cotraining Makes Strong Semi-Supervised SegmentorCode1
Covariance Matrix Adaptation for the Rapid Illumination of Behavior SpaceCode1
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