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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 40914100 of 9051 papers

TitleStatusHype
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems0
Diverse Audio Captioning via Adversarial Training0
Diverse and Relevant Visual Storytelling with Scene Graph Embeddings0
GrACE: Generation using Associated Code Edits0
Black Feminist Musings on Algorithmic Oppression0
A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes0
How to Build Robust FAQ Chatbot with Controllable Question Generator?0
Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science0
Diverse and Lifespan Facial Age Transformation Synthesis with Identity Variation Rationality Metric0
Diverse and Fine-Grained Instruction-Following Ability Exploration with Synthetic Data0
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