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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 211220 of 9051 papers

TitleStatusHype
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language ModelsCode2
MAT: Mask-Aware Transformer for Large Hole Image InpaintingCode2
Boosting Latent Diffusion with Flow MatchingCode2
MegaMath: Pushing the Limits of Open Math CorporaCode2
Diffusion Models for Molecules: A Survey of Methods and TasksCode2
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerceCode2
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal SystemsCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
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