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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 7726–7750 of 9051 papers

TitleStatusHype
Understanding Diversity in Session-Based RecommendationCode0
Embracing Language Inclusivity and Diversity in CLIP through Continual Language LearningCode0
CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language PromptingCode0
Improving Generalization with Domain Convex GameCode0
E-Gen: Leveraging E-Graphs to Improve Continuous Representations of Symbolic ExpressionsCode0
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial RobustnessCode0
Select-Mosaic: Data Augmentation Method for Dense Small Object ScenesCode0
Self-adaptation of Genetic Operators Through Genetic Programming TechniquesCode0
Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track CharacteristicsCode0
Activation Maximization Generative Adversarial NetsCode0
Cultural Heritage 3D Reconstruction with Diffusion NetworksCode0
Perceptual Similarity for Measuring Decision-Making Style and Policy Diversity in GamesCode0
Towards Better Open-Ended Text Generation: A Multicriteria Evaluation FrameworkCode0
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural FeaturesCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-BoundCode0
Using Set Covering to Generate Databases for Holistic SteganalysisCode0
Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across LanguagesCode0
Cultural-Aware AI Model for Emotion RecognitionCode0
Performative Recommendation: Diversifying Content via Strategic IncentivesCode0
Large-Scale Auto-Regressive Modeling Of Street NetworksCode0
Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data GenerationCode0
Cultivating Archipelago of Forests: Evolving Robust Decision Trees through Island CoevolutionCode0
Improving End-to-End Sequential Recommendations with Intent-aware DiversificationCode0
Improving Diversity of Commonsense Generation by Large Language Models via In-Context LearningCode0
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