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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 201210 of 9051 papers

TitleStatusHype
Ambiguous Medical Image Segmentation using Diffusion ModelsCode2
LaMAR: Benchmarking Localization and Mapping for Augmented RealityCode2
Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMsCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
Learnable Item Tokenization for Generative RecommendationCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics EmulationCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
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