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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

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Showing 7051–7075 of 9051 papers

TitleStatusHype
Re-ranking Based Diversification: A Unifying View—0
Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System—0
UPCS: Unbiased Persona Construction for Dialogue Generation—0
ResBench: Benchmarking LLM-Generated FPGA Designs with Resource Awareness—0
Research Experience of an Undergraduate Student in Computer Vision and Robotics—0
Research on Dynamic Data Flow Anomaly Detection based on Machine Learning—0
An ensemble diversity approach to supervised binary hashing—0
Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging—0
An Enhancement of Jiang, Z., et al.s Compression-Based Classification Algorithm Applied to News Article Categorization—0
An Energy Activity Dataset for Smart Homes—0
Resolving Multi-path Interference in Time-of-Flight Imaging via Modulation Frequency Diversity and Sparse Regularization—0
Adaptive pruning-based Newton's method for distributed learning—0
Resource Allocation in 6G Optical Wireless Systems—0
Resource and data efficient self supervised learning—0
Resource-Constrained Edge AI with Early Exit Prediction—0
Resource Mention Extraction for MOOC Discussion Forums—0
Resources for Multilingual Hate Speech Detection—0
UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation—0
Response of Different Tomato Accessions to Biotic and Abiotic Stresses—0
Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation—0
An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards—0
An Empirical Study of Group Conformity in Multi-Agent Systems—0
Restricting Greed in Training of Generative Adversarial Network—0
Navigating Text-to-Image Generative Bias across Indic Languages—0
An Empirical Study of Finding Similar Exercises—0
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