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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 13611370 of 9051 papers

TitleStatusHype
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business DocumentsCode1
Calliar: An Online Handwritten Dataset for Arabic CalligraphyCode1
C^2: Scalable Auto-Feedback for LLM-based Chart GenerationCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Language-Grounded Indoor 3D Semantic Segmentation in the WildCode1
Language-guided Human Motion Synthesis with Atomic ActionsCode1
Contextual Diversity for Active LearningCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
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