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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 4150 of 9051 papers

TitleStatusHype
Enhancing Chat Language Models by Scaling High-quality Instructional ConversationsCode4
3D Scene Generation: A SurveyCode4
Distill Any Depth: Distillation Creates a Stronger Monocular Depth EstimatorCode4
Improving Text Embeddings with Large Language ModelsCode3
INTERS: Unlocking the Power of Large Language Models in Search with Instruction TuningCode3
LongAlign: A Recipe for Long Context Alignment of Large Language ModelsCode3
Improved motif-scaffolding with SE(3) flow matchingCode3
Hierarchical Text-Conditional Image Generation with CLIP LatentsCode3
Improving Model Evaluation using SMART Filtering of Benchmark DatasetsCode3
GenWarp: Single Image to Novel Views with Semantic-Preserving Generative WarpingCode3
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