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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 3691–3700 of 9051 papers

TitleStatusHype
Concept Drift Adaptation by Exploiting Historical Knowledge—0
A Simulation-Optimization Technique for Service Level Analysis in Conjunction with Reorder Point Estimation and Lead-Time consideration: A Case Study in Sea Port—0
Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model—0
Exploring Novel Quality Diversity Methods For Generalization in Reinforcement Learning—0
Exploring Non-Linear Effects of Built Environment on Travel Using an Integrated Machine Learning and Inferential Modeling Approach: A Three-Wave Repeated Cross-Sectional Study—0
Simple yet Effective Way for Improving the Performance of GAN—0
A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction—0
Exploring Language Patterns of Prompts in Text-to-Image Generation and Their Impact on Visual Diversity—0
Computing High-Quality Solutions for the Patient Admission Scheduling Problem using Evolutionary Diversity Optimisation—0
Exploring Implicit Sentiment Evoked by Fine-grained News Events—0
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