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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 24612470 of 9051 papers

TitleStatusHype
DragTraffic: Interactive and Controllable Traffic Scene Generation for Autonomous Driving0
Language-Driven Active Learning for Diverse Open-Set 3D Object DetectionCode0
Parameter Efficient Diverse Paraphrase Generation Using Sequence-Level Knowledge Distillation0
MAexp: A Generic Platform for RL-based Multi-Agent ExplorationCode2
Understanding the genetic basis of variation in meiotic recombination: past, present, and future0
Global Counterfactual DirectionsCode0
How Population Diversity Influences the Efficiency of Crossover0
Evaluating AI for Law: Bridging the Gap with Open-Source Solutions0
One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model DiversityCode0
Tailoring Generative Adversarial Networks for Smooth Airfoil Design0
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