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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 4981–4990 of 9051 papers

TitleStatusHype
Origin of power laws and their spatial fractal structure for city-size distributions—0
Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?—0
Fine-grained Activities of People Worldwide—0
Multimodal Multi-objective Optimization: Comparative Study of the State-of-the-ArtCode1
SummScore: A Comprehensive Evaluation Metric for Summary Quality Based on Cross-Encoder—0
Learning an evolved mixture model for task-free continual learning—0
Interaction Pattern Disentangling for Multi-Agent Reinforcement LearningCode1
PoseGU: 3D Human Pose Estimation with Novel Human Pose Generator and Unbiased Learning—0
Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution StrategiesCode0
Learning to Diversify for Product Question Generation—0
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