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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 48214830 of 9051 papers

TitleStatusHype
MDM: Molecular Diffusion Model for 3D Molecule Generation0
Analysis of Self-Attention Head Diversity for Conformer-based Automatic Speech Recognition0
Style Variable and Irrelevant Learning for Generalizable Person Re-identificationCode0
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes0
Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path QualityCode1
Diversity and Novelty MasterPrints: Generating Multiple DeepMasterPrints for Increased User Coverage0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis0
Self-supervised Human Mesh Recovery with Cross-Representation Alignment0
Towards Diversity-tolerant RDF-storesCode0
Improved Masked Image Generation with Token-Critic0
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