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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 3140 of 9051 papers

TitleStatusHype
GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy PredictionCode4
Quality-aware Masked Diffusion Transformer for Enhanced Music GenerationCode4
ActionStudio: A Lightweight Framework for Data and Training of Large Action ModelsCode4
Distill Any Depth: Distillation Creates a Stronger Monocular Depth EstimatorCode4
A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANsCode4
Efficient Part-level 3D Object Generation via Dual Volume PackingCode4
Enhancing Chat Language Models by Scaling High-quality Instructional ConversationsCode4
AlphaFold Meets Flow Matching for Generating Protein EnsemblesCode4
Choices are More Important than Efforts: LLM Enables Efficient Multi-Agent ExplorationCode4
3D Scene Generation: A SurveyCode4
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