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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 1361–1370 of 9051 papers

TitleStatusHype
Distribution-Aware Compensation Design for Sustainable Data Rights in Machine Learning—0
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model—0
GDPO: Learning to Directly Align Language Models with Diversity Using GFlowNets—0
SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspection—0
Compression using Discrete Multi-Level Divisor Transform for Heterogeneous Sensor Data—0
Soft-Label Integration for Robust Toxicity ClassificationCode0
DFlow: Diverse Dialogue Flow Simulation with Large Language Models—0
MetaAlign: Align Large Language Models with Diverse Preferences during Inference TimeCode0
Measuring Diversity: Axioms and Challenges—0
How Does Data Diversity Shape the Weight Landscape of Neural Networks?—0
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