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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 2811–2820 of 9051 papers

TitleStatusHype
MGF: Mixed Gaussian Flow for Diverse Trajectory PredictionCode1
Heterogeneity-aware Cross-school Electives Recommendation: a Hybrid Federated Approach—0
IRR: Image Review Ranking Framework for Evaluating Vision-Language Models—0
PreAct: Prediction Enhances Agent's Planning AbilityCode1
ReViT: Enhancing Vision Transformers Feature Diversity with Attention Residual ConnectionsCode1
MONAL: Model Autophagy Analysis for Modeling Human-AI Interactions—0
HEAL: Brain-inspired Hyperdimensional Efficient Active Learning—0
Semi-supervised Medical Image Segmentation Method Based on Cross-pseudo Labeling Leveraging Strong and Weak Data Augmentation StrategiesCode1
RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data—0
Instruction Diversity Drives Generalization To Unseen Tasks—0
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