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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 22212230 of 9051 papers

TitleStatusHype
Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member ModelsCode0
GRATIS: GeneRAting TIme Series with diverse and controllable characteristicsCode0
Capturing the diversity of multilingual societiesCode0
Gram-Elites: N-Gram Based Quality-Diversity SearchCode0
Graph-guided Architecture Search for Real-time Semantic SegmentationCode0
Gradient Estimators for Implicit ModelsCode0
Growing Artificial Neural Networks for Control: the Role of Neuronal DiversityCode0
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the WildCode0
Go Back in Time: Generating Flashbacks in Stories with Event Temporal PromptsCode0
GPoeT-2: A GPT-2 Based Poem GeneratorCode0
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