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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 6776–6800 of 9051 papers

TitleStatusHype
Design Diversity for Improving Efficiency and Reducing Risk in Oil and Gas Well Stimulation under Uncertain Reservoir Conditions—0
Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics—0
Event Detection: Gate Diversity and Syntactic Importance Scoresfor Graph Convolution Neural Networks—0
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RLCode1
Advancing Non-Contact Vital Sign Measurement using Synthetic Avatars—0
Out-of-distribution detection for regression tasks: parameter versus predictor entropy—0
Compressed Sensing with Approximate Priors via Conditional Resampling—0
Performance Analysis of Intelligent Reflective Surface Aided Wireless Communications—0
Towards Robust Neural Networks via Orthogonal DiversityCode0
Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems—0
Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem—0
Improving Generalization in Reinforcement Learning with Mixture RegularizationCode1
Diversity in immunogenomics: the value and the challenge—0
Explorable Tone Mapping Operators—0
Promoting High Diversity Ensemble Learning with EnsembleBenchCode1
A study of quality and diversity in K+1 GANs—0
Understanding Unnatural Questions Improves Reasoning over Text—0
Fractal Autoencoders for Feature SelectionCode1
Synthesizing the Unseen for Zero-shot Object DetectionCode1
Neural-Driven Multi-criteria Tree Search for Paraphrase Generation—0
PLS for V2I Communications Using Friendly Jammer and Double kappa-mu Shadowed FadingCode0
Aggregating Dependent Gaussian Experts in Local Approximation—0
Ensemble Kalman Variational Objectives: Nonlinear Latent Trajectory Inference with A Hybrid of Variational Inference and Ensemble Kalman FilterCode0
Towards Accurate Human Pose Estimation in Videos of Crowded Scenes—0
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
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