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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 11911200 of 9051 papers

TitleStatusHype
Accelerating Score-based Generative Models with Preconditioned Diffusion SamplingCode1
C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot FillingCode1
General and Task-Oriented Video SegmentationCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
Generating Smooth Pose Sequences for Diverse Human Motion PredictionCode1
Bootstrapping Referring Multi-Object TrackingCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion ModelsCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
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