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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 251260 of 9051 papers

TitleStatusHype
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based RefinementCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World UsersCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D GenerationCode2
DiffusionPen: Towards Controlling the Style of Handwritten Text GenerationCode2
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
Delta Decompression for MoE-based LLMs CompressionCode2
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal SystemsCode2
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