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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 891900 of 9051 papers

TitleStatusHype
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable DiffusionCode1
LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form ControlCode1
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving PlannersCode1
ANTM: An Aligned Neural Topic Model for Exploring Evolving TopicsCode1
Evolving Flying Machines in Minecraft Using Quality DiversityCode1
ProtoSeg: Interpretable Semantic Segmentation with Prototypical PartsCode1
Data Augmentation Alone Can Improve Adversarial TrainingCode1
Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon PredictionCode1
Active learning for medical image segmentation with stochastic batchesCode1
Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesCode1
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