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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

Showing 47214730 of 9051 papers

TitleStatusHype
E2R: a Hierarchical-Learning inspired Novelty-Search method to generate diverse repertoires of grasping trajectories0
Query Expansion Using Contextual Clue Sampling with Language Models0
A Novel Multi-Objective Velocity-Free Boolean Particle Swarm Optimization0
Can we use Common Voice to train a Multi-Speaker TTS system?Code1
What Makes Graph Neural Networks Miscalibrated?Code1
Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction0
Near-Optimal Multi-Agent Learning for Safe Coverage ControlCode1
SQuId: Measuring Speech Naturalness in Many Languages0
Measuring and Improving Semantic Diversity of Dialogue GenerationCode0
BanglaParaphrase: A High-Quality Bangla Paraphrase DatasetCode1
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