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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 161170 of 9051 papers

TitleStatusHype
Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation ModelsCode2
Flow of Reasoning:Training LLMs for Divergent Problem Solving with Minimal ExamplesCode2
VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term ModelingCode2
Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language ModelsCode2
Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2Code2
Diffusion Bridge Implicit ModelsCode2
Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in MammographyCode2
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
Grounded 3D-LLM with Referent TokensCode2
DiverGen: Improving Instance Segmentation by Learning Wider Data Distribution with More Diverse Generative DataCode2
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