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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

Showing 83518375 of 9051 papers

TitleStatusHype
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI ModelsCode0
Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred LearningCode0
Sometimes I am a Tree: Data Drives Unstable Hierarchical GeneralizationCode0
"Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?Code0
MineRL: A Large-Scale Dataset of Minecraft DemonstrationsCode0
GAN-GA: A Generative Model based on Genetic Algorithm for Medical Image GenerationCode0
Game Theory for Adversarial Attacks and DefensesCode0
G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-TrainingCode0
FuncGenFoil: Airfoil Generation and Editing Model in Function SpaceCode0
The Galactic Dependencies Treebanks: Getting More Data by Synthesizing New LanguagesCode0
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly DetectionCode0
Quality-Diversity Meta-Evolution: customising behaviour spaces to a meta-objectiveCode0
Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory LearnersCode0
Diversified Arbitrary Style Transfer via Deep Feature PerturbationCode0
SonOpt: Sonifying Bi-objective Population-Based Optimization AlgorithmsCode0
Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent DiscoveryCode0
Diversification in Session-based News Recommender SystemsCode0
Fully Automatic Video Colorization with Self-Regularization and DiversityCode0
Full-Stack Filters to Build Minimum Viable CNNsCode0
Quality Diversity Through SurpriseCode0
Diverse Weighted Bipartite b-MatchingCode0
Quality-Diversity with Limited ResourcesCode0
FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing FlowCode0
MirrorGAN: Learning Text-to-image Generation by RedescriptionCode0
Quality Evolvability ES: Evolving Individuals With a Distribution of Well Performing and Diverse OffspringCode0
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