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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 37513760 of 9051 papers

TitleStatusHype
A Simple Measure of Economic Complexity0
FlexNER: A Flexible LSTM-CNN Stack Framework for Named Entity Recognition0
Exploring Diverse Expressions for Paraphrase Generation0
Computational historical linguistics and language diversity in South Asia0
Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback0
Exploring Data Augmentations on Self-/Semi-/Fully- Supervised Pre-trained Models0
Computational historical linguistics and language diversity in South Asia0
Exploring Critical Testing Scenarios for Decision-Making Policies: An LLM Approach0
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation0
Exploring Attribute Variations in Style-based GANs using Diffusion Models0
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