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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

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Showing 4291–4300 of 9051 papers

TitleStatusHype
Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image AnalysisCode1
DEff-GAN: Diverse Attribute Transfer for Few-Shot Image SynthesisCode0
Finding Support Examples for In-Context Learning—0
Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets—0
Cross-modal Face- and Voice-style Transfer—0
Diversity matters: Robustness of bias measurements in WikidataCode0
Tailoring Language Generation Models under Total Variation DistanceCode1
Closed-loop Error Correction Learning Accelerates Experimental Discovery of Thermoelectric MaterialsCode0
Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition—0
Speech Corpora Divergence Based Unsupervised Data Selection for ASR—0
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