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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 3226–3250 of 9051 papers

TitleStatusHype
Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition—0
Diversity Analysis for Indoor Terahertz Communication Systems under Small-Scale Fading—0
Ensemble prosody prediction for expressive speech synthesis—0
Ensemble pruning via an integer programming approach with diversity constraints—0
Ensemble Pruning via Margin Maximization—0
Bregman Centroid Guided Cross-Entropy Method—0
3D Neural Field Generation using Triplane Diffusion—0
Ensembles of GANs for synthetic training data generation—0
Coherent Visual Storytelling via Parallel Top-Down Visual and Topic Attention—0
Exploring Variational Autoencoders for Medical Image Generation: A Comprehensive Study—0
Ensembles of Randomized NNs for Pattern-based Time Series Forecasting—0
CoinRobot: Generalized End-to-end Robotic Learning for Physical Intelligence—0
Ensembles of Random SHAPs—0
Diversity-Achieving Slow-DropBlock Network for Person Re-Identification—0
DiversiTree: A New Method to Efficiently Compute Diverse Sets of Near-Optimal Solutions to Mixed-Integer Optimization Problems—0
An investigation into language complexity of World-of-Warcraft game-external texts—0
Ensemble Squared: A Meta AutoML System—0
Ensembling Sparse Autoencoders—0
Co-Learning Bayesian Optimization—0
Entailment-Preserving First-order Logic Representations in Natural Language Entailment—0
Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks—0
Breaking the mold: The challenge of large scale MARL specialization—0
Entropic Distribution Matching in Supervised Fine-tuning of LLMs: Less Overfitting and Better Diversity—0
Entropy and Diversity: The Axiomatic Approach—0
Advanced Framework for Animal Sound Classification With Features Optimization—0
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