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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 11911200 of 9051 papers

TitleStatusHype
Multi-Perspective Stance DetectionCode0
CorrSynth -- A Correlated Sampling Method for Diverse Dataset Generation from LLMs0
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQLCode4
Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation0
PerceiverS: A Multi-Scale Perceiver with Effective Segmentation for Long-Term Expressive Symbolic Music Generation0
Study on Inter and Intra Speaker Variability in Speaker Recognition0
Fair Summarization: Bridging Quality and Diversity in Extractive SummariesCode0
Top-nσ: Not All Logits Are You Need0
Integrating Chaotic Evolutionary and Local Search Techniques in Decision Space for Enhanced Evolutionary Multi-Objective Optimization0
Mix from Failure: Confusion-Pairing Mixup for Long-Tailed Recognition0
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