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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 2130 of 9051 papers

TitleStatusHype
Fake News Detection: It's All in the Data!Code5
OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive AnnotationsCode5
DepthCrafter: Generating Consistent Long Depth Sequences for Open-world VideosCode5
GAPartManip: A Large-scale Part-centric Dataset for Material-Agnostic Articulated Object ManipulationCode5
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-TuningCode5
ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity PreservingCode5
MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge AggregationCode5
BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting ModelsCode5
Enhancing Chat Language Models by Scaling High-quality Instructional ConversationsCode4
3D Scene Generation: A SurveyCode4
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