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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 44914500 of 9051 papers

TitleStatusHype
Integrating User History into Heterogeneous Graph for Dialogue Act Recognition0
Intelligence Graph0
IntenT5: Search Result Diversification using Causal Language Models0
DE/RM-MEDA: A New Hybrid Multi-Objective Generator0
Analysis of Self-Attention Head Diversity for Conformer-based Automatic Speech Recognition0
Spatial Structure Supports Diversity in Prebiotic Autocatalytic Chemical Ecosystems0
Beefmoves: Dissemination, Diversity, and Dynamics of English Borrowings in a German Hip Hop Forum0
Unsourced Adversarial CAPTCHA: A Bi-Phase Adversarial CAPTCHA Framework0
Becoming More Robust to Label Noise with Classifier Diversity0
Density Descent for Diversity Optimization0
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