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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

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Showing 2071–2080 of 9051 papers

TitleStatusHype
Data Collection and Labeling Techniques for Machine Learning—0
Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective—0
Open Generative Large Language Models for Galician—0
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments—0
RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone DesignCode2
TourLLM: Enhancing LLMs with Tourism KnowledgeCode0
Nash CoT: Multi-Path Inference with Preference EquilibriumCode0
AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image ClassificationCode2
Top-Down Bayesian Posterior Sampling for Sum-Product Networks—0
LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations—0
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