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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 101110 of 9051 papers

TitleStatusHype
GroundingSuite: Measuring Complex Multi-Granular Pixel GroundingCode2
ProtComposer: Compositional Protein Structure Generation with 3D EllipsoidsCode2
DivPrune: Diversity-based Visual Token Pruning for Large Multimodal ModelsCode2
Delta Decompression for MoE-based LLMs CompressionCode2
Diffusion Models for Molecules: A Survey of Methods and TasksCode2
Knowledge Graph-Guided Retrieval Augmented GenerationCode2
Compressed Image Generation with Denoising Diffusion Codebook ModelsCode2
Diverse Preference OptimizationCode2
General Scene Adaptation for Vision-and-Language NavigationCode2
Visual Generation Without GuidanceCode2
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