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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

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Showing 1631–1640 of 9051 papers

TitleStatusHype
Diversity-Driven View Subset Selection for Indoor Novel View SynthesisCode0
Table-to-Text Generation with Pretrained Diffusion Models—0
MathGLM-Vision: Solving Mathematical Problems with Multi-Modal Large Language Model—0
Human Motion Synthesis_ A Diffusion Approach for Motion Stitching and In-Betweening—0
Automated Data Augmentation for Few-Shot Time Series Forecasting: A Reinforcement Learning Approach Guided by a Model Zoo—0
HybridFC: A Hybrid Fact-Checking Approach for Knowledge GraphsCode0
Advancing Topic Segmentation of Broadcasted Speech with Multilingual Semantic EmbeddingsCode0
AbGPT: De Novo Antibody Design via Generative Language ModelingCode1
Elucidating Optimal Reward-Diversity Tradeoffs in Text-to-Image Diffusion Models—0
UAVDB: Trajectory-Guided Adaptable Bounding Boxes for UAV DetectionCode1
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