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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 111120 of 9051 papers

TitleStatusHype
Test-time Alignment of Diffusion Models without Reward Over-optimizationCode2
Magic Mirror: ID-Preserved Video Generation in Video Diffusion TransformersCode2
RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented InstructionsCode2
RecLM: Recommendation Instruction TuningCode2
A Generalizable Anomaly Detection Method in Dynamic GraphsCode2
AnySat: One Earth Observation Model for Many Resolutions, Scales, and ModalitiesCode2
Guiding Generative Protein Language Models with Reinforcement LearningCode2
EvalGIM: A Library for Evaluating Generative Image ModelsCode2
DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image InpaintingCode2
AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data GenerationCode2
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