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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
AlphaFold Meets Flow Matching for Generating Protein EnsemblesCode4
Efficient Part-level 3D Object Generation via Dual Volume PackingCode4
ActionStudio: A Lightweight Framework for Data and Training of Large Action ModelsCode4
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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