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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 181190 of 9051 papers

TitleStatusHype
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based RefinementCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D GenerationCode2
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion ModelsCode2
Grounded 3D-LLM with Referent TokensCode2
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
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