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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 13311340 of 9051 papers

TitleStatusHype
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Controllable Group Choreography using Contrastive DiffusionCode1
IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language UnderstandingCode1
Inducing High Energy-Latency of Large Vision-Language Models with Verbose ImagesCode1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Contrastive Syn-to-Real GeneralizationCode1
Input-Aware Dynamic Backdoor AttackCode1
Controllable Multi-Interest Framework for RecommendationCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
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