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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 271280 of 9051 papers

TitleStatusHype
Delta Decompression for MoE-based LLMs CompressionCode2
DiffusionPen: Towards Controlling the Style of Handwritten Text GenerationCode2
A Generalizable Anomaly Detection Method in Dynamic GraphsCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
DivPrune: Diversity-based Visual Token Pruning for Large Multimodal ModelsCode2
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-TrainingCode2
DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image GenerationCode2
A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous SpeechCode2
Curvature Diversity-Driven Deformation and Domain Alignment for Point CloudCode2
DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single ImageCode2
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