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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
A Generalizable Anomaly Detection Method in Dynamic GraphsCode2
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal SystemsCode2
DiffusionPen: Towards Controlling the Style of Handwritten Text GenerationCode2
DiverGen: Improving Instance Segmentation by Learning Wider Data Distribution with More Diverse Generative DataCode2
Diverse Preference OptimizationCode2
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-TrainingCode2
DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image InpaintingCode2
AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data GenerationCode2
DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single ImageCode2
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