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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 28712880 of 9051 papers

TitleStatusHype
Fair and Diverse DPP-based Data SummarizationCode0
Exploring Format Consistency for Instruction TuningCode0
Deep Hashing with Category Mask for Fast Video RetrievalCode0
Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution StrategiesCode0
Exploring Flat Minima for Domain Generalization with Large Learning RatesCode0
Neural-based Modeling for Performance Tuning of Spark Data AnalyticsCode0
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous DrivingCode0
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class AnnealingCode0
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
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