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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 221230 of 9051 papers

TitleStatusHype
MusiConGen: Rhythm and Chord Control for Transformer-Based Text-to-Music GenerationCode2
NAVIX: Scaling MiniGrid Environments with JAXCode2
Diverse Preference OptimizationCode2
OmniSat: Self-Supervised Modality Fusion for Earth ObservationCode2
EgoMimic: Scaling Imitation Learning via Egocentric VideoCode2
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
Palette: Image-to-Image Diffusion ModelsCode2
Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and TrackingCode2
Can Go AIs be adversarially robust?Code2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
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