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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 5826–5850 of 9051 papers

TitleStatusHype
WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation—0
Neural Network Ensembles: Theory, Training, and the Importance of Explicit Diversity—0
Diversity of growth rates maximizes phytoplankton productivity in an eddying ocean—0
DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal GraphsCode0
Making Curiosity Explicit in Vision-based RL—0
Exploratory State Representation LearningCode0
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning—0
Audio-to-Image Cross-Modal Generation—0
Automatic Generation of Word Problems for Academic Education via Natural Language Processing (NLP)Code0
MUTEN: Boosting Gradient-Based Adversarial Attacks via Mutant-Based Ensembles—0
Review of Clustering-Based Recommender Systems—0
Parallel Refinements for Lexically Constrained Text Generation with BARTCode1
Smartphone Data Reveal Neighborhood-Level Racial Disparities in Police Presence—0
Stacked Ensemble Machine Learning for Range-Separation ParametersCode0
Human genetic admixture through the lens of population genomics—0
A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification—0
Internal Feedback in Biological Control: Diversity, Delays, and Standard Theory—0
The Beauty Everywhere: How Aesthetic Criteria Contribute to the Development of AI—0
Stigmergy-based collision-avoidance algorithm for self-organising swarms—0
Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-IdentificationCode1
Rebuilding Trust: Queer in AI Approach to Artificial Intelligence Risk Management—0
Search For Deep Graph Neural Networks—0
Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation—0
Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision—0
Variational Embedding Multiscale Sample Entropy:complexity-based analysis for multichannel systems—0
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