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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 8676–8700 of 9051 papers

TitleStatusHype
Diversity of meso-scale architecture in human and non-human connectomes—0
Diversity of preferences can increase collective welfare in sequential exploration problems—0
Diversity of Thought Elicits Stronger Reasoning Capabilities in Multi-Agent Debate Frameworks—0
Diversity of Thought Improves Reasoning Abilities of LLMs—0
Diversity of Transformer Layers: One Aspect of Parameter Scaling Laws—0
Diversity of What? On the Different Conceptualizations of Diversity in Recommender Systems—0
A Deep Generative Model for Feasible and Diverse Population Synthesis—0
Diversity-Oriented Data Augmentation with Large Language Models—0
Diversity Over Quantity: A Lesson From Few Shot Relation Classification—0
TANGLED: Generating 3D Hair Strands from Images with Arbitrary Styles and Viewpoints—0
Diversity Preference-Aware Link Recommendation for Online Social Networks—0
Diversity Preferences, Affirmative Action and Choice Rules—0
Diversity-Preserving K-Armed Bandits, Revisited—0
Diversity Progress for Goal Selection in Discriminability-Motivated RL—0
Diversity-Promoting Bayesian Learning of Latent Variable Models—0
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation—0
Diversity-Promoting Ensemble for Medical Image Segmentation—0
A Decomposition-Based Many-Objective Evolutionary Algorithm with Local Iterative Update—0
Diversity-Promoting Human Motion Interpolation via Conditional Variational Auto-Encoder—0
Diversity Promoting Online Sampling for Streaming Video Summarization—0
Diversity regularization in deep ensembles—0
Diversity Regularized Adversarial Learning—0
Diversity Regularized Interests Modeling for Recommender Systems—0
Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification—0
Diversity-Rewarded CFG Distillation—0
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