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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 1120 of 9051 papers

TitleStatusHype
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation DatasetCode7
MaskSketch: Unpaired Structure-guided Masked Image GenerationCode7
Is Diversity All You Need for Scalable Robotic Manipulation?Code7
Better Synthetic Data by Retrieving and Transforming Existing DatasetsCode7
Flow-GRPO: Training Flow Matching Models via Online RLCode7
From Audio to Photoreal Embodiment: Synthesizing Humans in ConversationsCode7
Automatic Chain of Thought Prompting in Large Language ModelsCode6
ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity PreservingCode5
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-TuningCode5
BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting ModelsCode5
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