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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 4926–4950 of 9051 papers

TitleStatusHype
Information Gain Sampling for Active Learning in Medical Image Classification—0
INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical ExamplesCode0
Tackling Neural Architecture Search With Quality Diversity OptimizationCode0
Smoothing Entailment Graphs with Language ModelsCode0
Streaming Algorithms for Diversity Maximization with Fairness ConstraintsCode0
Uncertainty-Driven Action Quality Assessment—0
Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity—0
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
Graph Inverse Reinforcement Learning from Diverse Videos—0
Diversity Boosted Learning for Domain Generalization with Large Number of Domains—0
LAD: Language Models as Data for Zero-Shot DialogCode0
Analysis of Quality Diversity Algorithms for the Knapsack Problem—0
Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem—0
Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendation—0
Computing High-Quality Solutions for the Patient Admission Scheduling Problem using Evolutionary Diversity Optimisation—0
Class-Aware Universum Inspired Re-Balance Learning for Long-Tailed Recognition—0
Distribution Learning Based on Evolutionary Algorithm Assisted Deep Neural Networks for Imbalanced Image Classification—0
Generalized Probabilistic U-Net for medical image segementationCode1
Static and Dynamic Concepts for Self-supervised Video Representation LearningCode1
Classifier-Free Diffusion GuidanceCode2
Bugs as Features (Part I): Concepts and Foundations for the Compositional Data Analysis of the Microbiome-Gut-Brain Axis—0
Representational Ethical Model Calibration—0
Domain-invariant Feature Exploration for Domain Generalization—0
CelebV-HQ: A Large-Scale Video Facial Attributes DatasetCode2
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification—0
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