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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 5626–5650 of 9051 papers

TitleStatusHype
MMRAG: Multi-Mode Retrieval-Augmented Generation with Large Language Models for Biomedical In-Context Learning—0
Attribute-guided Feature Extraction and Augmentation Robust Learning for Vehicle Re-identification—0
MMT: A Multilingual and Multi-Topic Indian Social Media Dataset—0
Attribute analysis with synthetic dataset for person re-identification—0
MOANA: Multi-Objective Ant Nesting Algorithm for Optimization Problems—0
Attentive Aspect Modeling for Review-aware Recommendation—0
Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation—0
Attention in Diffusion Model: A Survey—0
Model-Agnostic Meta-Learning for Multimodal Task Distributions—0
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning—0
Model-Based Clustering of Nonparametric Weighted Networks with Application to Water Pollution Analysis—0
Model-Based Quality-Diversity Search for Efficient Robot Learning—0
MODEL&CO: Exoplanet detection in angular differential imaging by learning across multiple observations—0
Transfer Learning-Enhanced Instantaneous Multi-Person Indoor Localization by CSI—0
Transfer learning for ensembles: reducing computation time and keeping the diversity—0
Model Compression with Generative Adversarial Networks—0
Transfer learning from High-Resource to Low-Resource Language Improves Speech Affect Recognition Classification Accuracy—0
Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans—0
Modeling cell differentiation in neuroblastoma: insights into development, malignancy, and treatment relapse—0
Modeling competitive evolution of multiple languages—0
Modeling Content Creator Incentives on Algorithm-Curated Platforms—0
Modeling Context Words as Regions: An Ordinal Regression Approach to Word Embedding—0
Modeling Cultural Dynamics—0
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning—0
Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder—0
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