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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 4026–4050 of 9051 papers

TitleStatusHype
Prompting Diverse Ideas: Increasing AI Idea Variance—0
Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport—0
DAM: Diffusion Activation Maximization for 3D Global ExplanationsCode0
The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness—0
Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images—0
Edge Conditional Node Update Graph Neural Network for Multi-variate Time Series Anomaly Detection—0
StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models—0
Diverse and Lifespan Facial Age Transformation Synthesis with Identity Variation Rationality Metric—0
Predictive Analysis for Optimizing Port Operations—0
From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning—0
GTAutoAct: An Automatic Datasets Generation Framework Based on Game Engine Redevelopment for Action Recognition—0
Multi-Agent Diagnostics for Robustness via Illuminated Diversity—0
Position: AI/ML Influencers Have a Place in the Academic Process—0
How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment—0
Style-Consistent 3D Indoor Scene Synthesis with Decoupled Objects—0
Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size—0
Benchmarking the Fairness of Image Upsampling MethodsCode0
UniHDA: A Unified and Versatile Framework for Multi-Modal Hybrid Domain Adaptation—0
Local Diversity of Condorcet DomainsCode0
An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction—0
SubgroupTE: Advancing Treatment Effect Estimation with Subgroup IdentificationCode0
Enhancing Recommendation Diversity by Re-ranking with Large Language Models—0
What Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenders—0
Measuring Policy Distance for Multi-Agent Reinforcement LearningCode0
Navigating the Thin Line: Examining User Behavior in Search to Detect Engagement and Backfire Effects—0
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