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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 6170 of 9051 papers

TitleStatusHype
Taming Diffusion Probabilistic Models for Character ControlCode3
UniMERNet: A Universal Network for Real-World Mathematical Expression RecognitionCode3
Addressing the Abstraction and Reasoning Corpus via Procedural Example GenerationCode3
UniTraj: A Unified Framework for Scalable Vehicle Trajectory PredictionCode3
ThemeStation: Generating Theme-Aware 3D Assets from Few ExemplarsCode3
Swin3D++: Effective Multi-Source Pretraining for 3D Indoor Scene UnderstandingCode3
LongAlign: A Recipe for Long Context Alignment of Large Language ModelsCode3
INTERS: Unlocking the Power of Large Language Models in Search with Instruction TuningCode3
Improved motif-scaffolding with SE(3) flow matchingCode3
EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture ModelingCode3
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