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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 441450 of 9051 papers

TitleStatusHype
Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text GenerationCode1
DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text GenerationCode1
Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identificationCode1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
Biological Sequence Design with GFlowNetsCode1
Bilingual Mutual Information Based Adaptive Training for Neural Machine TranslationCode1
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale AmbiguitiesCode1
AutoMix: Automatically Mixing Language ModelsCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object SegmentationCode1
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