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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 7426–7450 of 9051 papers

TitleStatusHype
Differential Evolution with Reversible Linear TransformationsCode1
Social diversity and social preferences in mixed-motive reinforcement learning—0
Automatic image-based identification and biomass estimation of invertebrates—0
Entropy Minimization vs. Diversity Maximization for Domain AdaptationCode1
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph EmbeddingCode1
Crowdsourcing the Perception of Machine Teaching—0
Variational Template Machine for Data-to-Text GenerationCode1
Improving the Evaluation of Generative Models with Fuzzy LogicCode0
Effective Diversity in Population Based Reinforcement LearningCode1
Music2Dance: DanceNet for Music-driven Dance Generation—0
Self-Adversarial Learning with Comparative Discrimination for Text Generation—0
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
Introducing the diagrammatic semiotic mode—0
The influence of diversity on the measurement of functional impairment: An international validation of the Amsterdam IADL Questionnaire in 8 countries—0
The Case for Bayesian Deep Learning—0
OPFython: A Python-Inspired Optimum-Path Forest ClassifierCode1
A hierarchical fusion framework integrating random projection-based classifiers: application in head and neck squamous carcinoma cancer—0
On the Role of Receptive Field in Unsupervised Sim-to-Real Image Translation—0
Finer Metagenomic Reconstruction via Biodiversity OptimizationCode0
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation—0
Learning Diverse Features with Part-Level Resolution for Person Re-IdentificationCode1
A Note on Species Richness and the Variance of Epidemic Severity—0
Measuring Diversity of Artificial Intelligence Conferences—0
Motion Classification using Kinematically Sifted ACGAN-Synthesized Radar Micro-Doppler Signatures—0
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural NetworksCode1
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