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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 15711580 of 9051 papers

TitleStatusHype
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text GenerationCode1
CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data DiversityCode1
Florence-2: Advancing a Unified Representation for a Variety of Vision TasksCode1
StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo MatchingCode1
Instance-Optimal Compressed Sensing via Posterior SamplingCode1
InternLM-Law: An Open Source Chinese Legal Large Language ModelCode1
FLUXSynID: A Framework for Identity-Controlled Synthetic Face Generation with Document and Live ImagesCode1
Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk DecodingCode1
Forecasting Future World Events with Neural NetworksCode1
KERPLE: Kernelized Relative Positional Embedding for Length ExtrapolationCode1
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