SOTAVerified

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 29812990 of 9051 papers

TitleStatusHype
EaSe: A Diagnostic Tool for VQA based on Answer DiversityCode0
Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity AlgorithmCode0
EventDrop: data augmentation for event-based learningCode0
A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger designCode0
AMPSO: Artificial Multi-Swarm Particle Swarm OptimizationCode0
Event Transition Planning for Open-ended Text GenerationCode0
Autoselection of the Ensemble of Convolutional Neural Networks with Second-Order Cone ProgrammingCode0
Evaluator for Emotionally Consistent ChatbotsCode0
ECO: Efficient Convolution Operators for TrackingCode0
Evolvability ES: Scalable and Direct Optimization of EvolvabilityCode0
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