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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 9911000 of 9051 papers

TitleStatusHype
Controllable Group Choreography using Contrastive DiffusionCode1
AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task GenerationCode1
Navigating Chemical Space with Latent FlowsCode1
Controllable Video Captioning with an Exemplar SentenceCode1
ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-Tree Complex Wavelet Representation and Contradict Channel LossCode1
Learning a Cross-modality Anomaly Detector for Remote Sensing ImageryCode1
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving PlannersCode1
Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual FidelityCode1
NeuralTailor: Reconstructing Sewing Pattern Structures from 3D Point Clouds of GarmentsCode1
Covariance Matrix Adaptation for the Rapid Illumination of Behavior SpaceCode1
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