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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 17711780 of 9051 papers

TitleStatusHype
Unsupervised Stereo Matching Network For VHR Remote Sensing Images Based On Error PredictionCode0
Leveraging Perceptual Scores for Dataset Pruning in Computer Vision Tasks0
Large Language Models Know What Makes Exemplary Contexts0
Diffusion Model for Slate Recommendation0
Chirped DFT-s-OFDM: A new single-carrier waveform with enhanced LMMSE noise suppression0
On a Scale-Invariant Approach to Bundle Recommendations in Candy Crush Saga0
Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents0
Hybrid SD: Edge-Cloud Collaborative Inference for Stable Diffusion Models0
SkillMimic: Learning Basketball Interaction Skills from DemonstrationsCode3
Improving Structural Diversity of Blackbox LLMs via Chain-of-Specification Prompting0
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