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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 181190 of 9051 papers

TitleStatusHype
VBR: A Vision Benchmark in RomeCode2
in2IN: Leveraging individual Information to Generate Human INteractionsCode2
OmniSat: Self-Supervised Modality Fusion for Earth ObservationCode2
Bridging Remote Sensors with Multisensor Geospatial Foundation ModelsCode2
Guide to k-mer approaches for genomics across the tree of lifeCode2
LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented DiffusionCode2
Protein Conformation Generation via Force-Guided SE(3) Diffusion ModelsCode2
MambaTalk: Efficient Holistic Gesture Synthesis with Selective State Space ModelsCode2
Face2Diffusion for Fast and Editable Face PersonalizationCode2
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-TrainingCode2
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