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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

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Showing 4931–4940 of 9051 papers

TitleStatusHype
Uncertainty-Driven Action Quality Assessment—0
Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity—0
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
Graph Inverse Reinforcement Learning from Diverse Videos—0
Diversity Boosted Learning for Domain Generalization with Large Number of Domains—0
LAD: Language Models as Data for Zero-Shot DialogCode0
Analysis of Quality Diversity Algorithms for the Knapsack Problem—0
Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem—0
Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendation—0
Computing High-Quality Solutions for the Patient Admission Scheduling Problem using Evolutionary Diversity Optimisation—0
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