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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 41314140 of 9051 papers

TitleStatusHype
Participatory Design of AI with Children: Reflections on IDC Design Challenge0
Revisiting k-NN for Fine-tuning Pre-trained Language ModelsCode1
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction0
ERTIM@MC2: Diversified Argumentative Tweets Retrieval0
CAViaR: Context Aware Video Recommendations0
Do you MIND? Reflections on the MIND dataset for research on diversity in news recommendations0
The MiniPile Challenge for Data-Efficient Language ModelsCode0
Test-Optional Admissions0
Context-aware Domain Adaptation for Time Series Anomaly Detection0
Text-Conditional Contextualized Avatars For Zero-Shot Personalization0
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