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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 941950 of 9051 papers

TitleStatusHype
A single-cell gene expression language modelCode1
S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAMCode1
Preference-Learning Emitters for Mixed-Initiative Quality-Diversity AlgorithmsCode1
Item-based Variational Auto-encoder for Fair Music RecommendationCode1
Multi-Objective GFlowNetsCode1
Generative Range Imaging for Learning Scene Priors of 3D LiDAR DataCode1
Spatio-channel Attention Blocks for Cross-modal Crowd CountingCode1
Optimizing Hierarchical Image VAEs for Sample QualityCode1
Intra-Source Style Augmentation for Improved Domain GeneralizationCode1
DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text GenerationCode1
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