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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 18011810 of 9051 papers

TitleStatusHype
Improving Screening Processes via Calibrated Subset SelectionCode0
A Simple, Fast Diverse Decoding Algorithm for Neural GenerationCode0
Computational detection of antigen specific B cell receptors following immunizationCode0
Improving the Data Efficiency of Multi-Objective Quality-Diversity through Gradient Assistance and Crowding ExplorationCode0
Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence PairsCode0
INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical ExamplesCode0
Is there a robust effect of mainland mutualism rates on species richness of oceanic islands?Code0
A Survey of Data Synthesis ApproachesCode0
Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-TuningCode0
Improving Neural Conversational Models with Entropy-Based Data FilteringCode0
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