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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 6051–6075 of 9051 papers

TitleStatusHype
Unsupervised Translation of Emergent Communication—0
Unsupervised User-Based Insider Threat Detection Using Bayesian Gaussian Mixture Models—0
Unsupervised vocal dereverberation with diffusion-based generative models—0
Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasing—0
CamoFA: A Learnable Fourier-based Augmentation for Camouflage Segmentation—0
Unveiling Temporal Trends in 19th Century Literature: An Information Retrieval Approach—0
Unveiling the Complexity of Neural Populations: Evaluating the Validity and Limitations of the Wilson-Cowan Model—0
Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms—0
UPCS: Unbiased Persona Construction for Dialogue Generation—0
Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging—0
UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation—0
Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation—0
UrduLLaMA 1.0: Dataset Curation, Preprocessing, and Evaluation in Low-Resource Settings—0
URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement—0
Use of Genome Information-Based Potentials to Characterize Human Adaptation—0
Use of Speech Impairment Severity for Dysarthric Speech Recognition—0
User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation—0
UserBoost: Generating User-specific Synthetic Data for Faster Enrolment into Behavioural Biometric Systems—0
User Clustering for Rate Splitting using Machine Learning—0
User-Creator Feature Polarization in Recommender Systems with Dual Influence—0
User Fairness in Recommender Systems—0
User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems—0
User Intention Recognition and Requirement Elicitation Method for Conversational AI Services—0
User Modeling for Task Oriented Dialogues—0
User-Movement-Robust Virtual Reality Through Dual-Beam Reception in mmWave Networks—0
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