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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 5101–5125 of 9051 papers

TitleStatusHype
Learning the Designer's Preferences to Drive Evolution—0
Learning the Parameters of Determinantal Point Process Kernels—0
Towards high-throughput 3D insect capture for species discovery and diagnostics—0
Learning to Act through Evolution of Neural Diversity in Random Neural Networks—0
A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels—0
Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation—0
Towards Human Evaluation of Mutual Understanding in Human-Computer Spontaneous Conversation: An Empirical Study of Word Sense Disambiguation for Naturalistic Social Dialogs in American English—0
Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 1—0
Learning to detect cloud and snow in remote sensing images from noisy labels—0
Learning to Detect Vehicles by Clustering Appearance Patterns—0
Learning to Diversify for Product Question Generation—0
Learning to Diversify Neural Text Generation via Degenerative Model—0
Learning to Diversify via Weighted Kernels for Classifier Ensemble—0
Learning to exploit z-Spatial Diversity for Coherent Nonlinear Optical Fiber Communication—0
Towards Imperceptible Query-limited Adversarial Attacks with Perceptual Feature Fidelity Loss—0
Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research—0
Balancing Effect of Training Dataset Distribution of Multiple Styles for Multi-Style Text Transfer—0
Balancing Creativity and Automation: The Influence of AI on Modern Film Production and Dissemination—0
Towards Inclusive Face Recognition Through Synthetic Ethnicity Alteration—0
Learning to Generate Videos Using Neural Uncertainty Priors—0
Less is more: Selecting informative and diverse subsets with balancing constraints—0
Learning to Identify Ambiguous and Misleading News Headlines—0
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework—0
Learning to Model Multimodal Semantic Alignment for Story Visualization—0
Learning to Predict Diverse Human Motions from a Single Image via Mixture Density Networks—0
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