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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 2901–2925 of 9051 papers

TitleStatusHype
Active Generation Network of Human Skeleton for Action Recognition—0
Embracing Language Inclusivity and Diversity in CLIP through Continual Language LearningCode0
NNOSE: Nearest Neighbor Occupational Skill ExtractionCode0
Analysis of Knowledge Tracing performance on synthesised student data—0
Pick-and-Draw: Training-free Semantic Guidance for Text-to-Image Personalization—0
KAUCUS: Knowledge Augmented User Simulators for Training Language Model Assistants—0
Contributing Dimension Structure of Deep Feature for Coreset SelectionCode0
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect—0
Prompting Diverse Ideas: Increasing AI Idea Variance—0
A Survey on 3D Skeleton Based Person Re-Identification: Approaches, Designs, Challenges, and Future Directions—0
Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport—0
DAM: Diffusion Activation Maximization for 3D Global ExplanationsCode0
Predictive Analysis for Optimizing Port Operations—0
On the Affinity, Rationality, and Diversity of Hierarchical Topic ModelingCode1
Revisiting Active Learning in the Era of Vision Foundation ModelsCode1
Diverse and Lifespan Facial Age Transformation Synthesis with Identity Variation Rationality Metric—0
StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models—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
The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness—0
Style-Consistent 3D Indoor Scene Synthesis with Decoupled Objects—0
GTAutoAct: An Automatic Datasets Generation Framework Based on Game Engine Redevelopment for Action Recognition—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
From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning—0
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