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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 3826–3850 of 9051 papers

TitleStatusHype
From Matching with Diversity Constraints to Matching with Regional Quotas—0
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning—0
Comprehensive Video Understanding: Video summarization with content-based video recommender design—0
From Posterior Sampling to Meaningful Diversity in Image Restoration—0
Exploiting Spatial-temporal Correlations for Video Anomaly Detection—0
Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition—0
EXPLOITING SEMANTIC COHERENCE TO IMPROVE PREDICTION IN SATELLITE SCENE IMAGE ANALYSIS: APPLICATION TO DISEASE DENSITY ESTIMATION—0
Exploiting Representation Bias for Data Distillation in Abstractive Text Summarization—0
From Shadow Segmentation to Shadow Removal—0
Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis—0
A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph—0
AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.0—0
Exploiting Knowledge Distillation for Few-Shot Image Generation—0
Exploiting Joint Robustness to Adversarial Perturbations—0
Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems—0
Exploiting Feature Diversity for Make-up Temporal Video Grounding—0
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning—0
Comprehensive Annotation of Multiword Expressions in a Social Web Corpus—0
Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling—0
Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images—0
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection—0
Exploiting Angular Multiplexing for Polarization-diversity in Off-axis Digital Holography—0
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching—0
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition—0
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