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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 15811590 of 9051 papers

TitleStatusHype
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language ModelsCode1
Revisiting k-NN for Fine-tuning Pre-trained Language ModelsCode1
AARGH! End-to-end Retrieval-Generation for Task-Oriented DialogCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous DrivingCode1
Deep Sketch-Based Modeling: Tips and TricksCode1
Robustness of Graph Neural Networks at ScaleCode1
RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style TransformationCode1
Elucidating the Design Space of Dataset CondensationCode1
Image Generation From Small Datasets via Batch Statistics AdaptationCode1
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