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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 341350 of 9051 papers

TitleStatusHype
Hierarchical Uncertainty-Aware Graph Neural Network0
Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments0
CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe GenerationCode0
Balancing Creativity and Automation: The Influence of AI on Modern Film Production and Dissemination0
DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning0
MATCHA: Can Multi-Agent Collaboration Build a Trustworthy Conversational Recommender?0
Offline Learning of Controllable Diverse Behaviors0
PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts0
Targeted AMP generation through controlled diffusion with efficient embeddings0
Robo-Troj: Attacking LLM-based Task Planners0
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