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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 28912900 of 9051 papers

TitleStatusHype
Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution StrategiesCode0
DPPy: Sampling DPPs with PythonCode0
Nondeterminism and Instability in Neural Network OptimizationCode0
Exploring Model Learning Heterogeneity for Boosting Ensemble RobustnessCode0
Exploring the Evolution of GANs through Quality DiversityCode0
Exploring Diversity in Back Translation for Low-Resource Machine TranslationCode0
Contributions of El Niño Southern Oscillation (ENSO) Diversity to Low-Frequency Changes in ENSO VarianceCode0
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous DrivingCode0
Exploring Flat Minima for Domain Generalization with Large Learning RatesCode0
Bags of Projected Nearest Neighbours: Competitors to Random Forests?Code0
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