SOTAVerified

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 361370 of 9051 papers

TitleStatusHype
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
AbGPT: De Novo Antibody Design via Generative Language ModelingCode1
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
Curriculum-guided Hindsight Experience ReplayCode1
DALNet: A Rail Detection Network Based on Dynamic Anchor LineCode1
Active Learning by Acquiring Contrastive ExamplesCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive ScenariosCode1
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning ParadigmCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
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