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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 28212830 of 9051 papers

TitleStatusHype
DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender SystemsCode0
Face Manifold: Manifold Learning for Synthetic Face GenerationCode0
BAL: Balancing Diversity and Novelty for Active LearningCode0
FaceCoresetNet: Differentiable Coresets for Face Set RecognitionCode0
Expressivity of Parameterized and Data-driven Representations in Quality Diversity SearchCode0
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic SegmentationCode0
Deep Metric Learning with BIER: Boosting Independent Embeddings RobustlyCode0
Can Users Detect Biases or Factual Errors in Generated Responses in Conversational Information-Seeking?Code0
Facilitating bootstrapped and rarefaction-based microbiome diversity analysis with q2-bootsCode0
Fairness and Diversity in Recommender Systems: A SurveyCode0
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