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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 13911400 of 9051 papers

TitleStatusHype
Controllable Multi-Interest Framework for RecommendationCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Length-Controllable Image CaptioningCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection BiasCode1
Leveraging Knowledge Bases And Parallel Annotations For Music Genre TranslationCode1
Lila: A Unified Benchmark for Mathematical ReasoningCode1
Can LLMs Patch Security Issues?Code1
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