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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 7676–7700 of 9051 papers

TitleStatusHype
Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation—0
Automatic difficulty management and testing in games using a framework based on behavior trees and genetic algorithmsCode0
A Corpus-free State2Seq User Simulator for Task-oriented DialogueCode0
An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue—0
The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale—0
Learning to Sample: an Active Learning Framework—0
Unsupervised Paraphrasing by Simulated Annealing—0
On the clustering of correlated random variables—0
An Algorithm for Multi-Attribute Diverse Matching—0
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks—0
The Benefits of Diversity: Permutation Recovery in Unlabeled Sensing from Multiple Measurement Vectors—0
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion AnalysisCode0
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation SystemsCode0
The Diversity-Innovation Paradox in ScienceCode0
Mixture Content Selection for Diverse Sequence GenerationCode0
Adversarial Bootstrapping for Dialogue Model Training—0
Cross-Cutting Political Awareness through Diverse News Recommendations—0
A Tool for Super-Resolving Multimodal Clinical MRICode0
Investigating the Relationship between Multi-Party Linguistic Entrainment, Team Characteristics, and the Perception of Team Social Outcomes—0
Relationship-Aware Spatial Perception Fusion for Realistic Scene Layout Generation—0
Dialect-Specific Models for Automatic Speech Recognition of African American Vernacular English—0
Unsupervised Data Augmentation for Less-Resourced Languages with no Standardized Spelling—0
Twitter Bot Detection using Diversity Measures—0
(Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple PersonasCode0
Object Detection in Optical Remote Sensing Images: A Survey and A New BenchmarkCode1
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