SOTAVerified

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 12911300 of 9051 papers

TitleStatusHype
Can pre-trained models assist in dataset distillation?Code1
Towards Geospatial Foundation Models via Continual PretrainingCode1
Global Tensor Motion PlanningCode1
DGRec: Graph Neural Network for Recommendation with Diversified Embedding GenerationCode1
Harvesting Event Schemas from Large Language ModelsCode1
Can 3D Vision-Language Models Truly Understand Natural Language?Code1
Towards Evaluating Generalist Agents: An Automated Benchmark in Open WorldCode1
Boosting Human-Object Interaction Detection with Text-to-Image Diffusion ModelCode1
GenPlot: Increasing the Scale and Diversity of Chart Derendering DataCode1
GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised LearningCode1
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