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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 28912900 of 9051 papers

TitleStatusHype
IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity RecognitionCode1
Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical SciencesCode0
Bias in Opinion Summarisation from Pre-training to Adaptation: A Case Study in Political Bias0
SCO-VIST: Social Interaction Commonsense Knowledge-based Visual Storytelling0
Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detection0
LLM Voting: Human Choices and AI Collective Decision MakingCode1
SPECTRUM: Speaker-Enhanced Pre-Training for Long Dialogue Summarization0
Topology-Aware Latent Diffusion for 3D Shape Generation0
LongAlign: A Recipe for Long Context Alignment of Large Language ModelsCode3
Large Language Models for Mathematical Reasoning: Progresses and Challenges0
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