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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 171180 of 9051 papers

TitleStatusHype
DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D GenerationCode2
CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
Learnable Item Tokenization for Generative RecommendationCode2
Multi-Space Alignments Towards Universal LiDAR SegmentationCode2
Benchmarking Representations for Speech, Music, and Acoustic EventsCode2
SynFlowNet: Design of Diverse and Novel Molecules with Synthesis ConstraintsCode2
IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic LanguagesCode2
The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language ModelsCode2
MAexp: A Generic Platform for RL-based Multi-Agent ExplorationCode2
Token-level Direct Preference OptimizationCode2
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