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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 24512460 of 9051 papers

TitleStatusHype
Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches0
A Survey on Self-Evolution of Large Language Models0
Collaborative Perception Datasets in Autonomous Driving: A Survey0
UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation0
Semantic-Rearrangement-Based Multi-Level Alignment for Domain Generalized Segmentation0
Elucidating the Design Space of Dataset CondensationCode1
Wills Aligner: Multi-Subject Collaborative Brain Visual Decoding0
Sentiment-oriented Transformer-based Variational Autoencoder Network for Live Video CommentingCode0
DragTraffic: Interactive and Controllable Traffic Scene Generation for Autonomous Driving0
Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning0
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