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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 7180 of 9051 papers

TitleStatusHype
Efficient Part-level 3D Object Generation via Dual Volume PackingCode4
When Is Diversity Rewarded in Cooperative Multi-Agent Learning?0
Intent Factored Generation: Unleashing the Diversity in Your Language ModelCode0
"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)0
GRAIL: A Benchmark for GRaph ActIve Learning in Dynamic Sensing Environments0
Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction0
Scalable and Cost-Efficient de Novo Template-Based Molecular GenerationCode1
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation0
The Cell Ontology in the age of single-cell omicsCode0
Exploration by Random Reward Perturbation0
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