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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 1876–1900 of 9051 papers

TitleStatusHype
AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations—0
DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous DrivingCode1
Diffusion for Out-of-Distribution Detection on Road Scenes and BeyondCode1
Visual-Semantic Decomposition and Partial Alignment for Document-based Zero-Shot LearningCode0
Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealingCode1
Beyond Size and Class Balance: Alpha as a New Dataset Quality Metric for Deep Learning—0
Semantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph GenerationCode1
MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive DiversityCode2
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning—0
MusiConGen: Rhythm and Chord Control for Transformer-Based Text-to-Music GenerationCode2
Downstream-Pretext Domain Knowledge Traceback for Active Learning—0
Strategic Coupon Allocation for Increasing Providers' Sales Experiences in Two-sided Marketplaces—0
Self-supervised transformer-based pre-training method with General Plant Infection datasetCode0
Similarity of Information and Collective Action—0
Towards Realistic Emotional Voice Conversion using Controllable Emotional Intensity—0
Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models—0
Automatic Generation of Fashion Images using Prompting in Generative Machine Learning ModelsCode0
AI for All: Identifying AI incidents Related to Diversity and Inclusion—0
A Comparative Study of Transfer Learning for Emotion Recognition using CNN and Modified VGG16 Models—0
The Structure of Occupational Mobility in France—0
Double Gradient Reversal Network for Single-Source Domain Generalization in Multi-mode Fault Diagnosis—0
Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth EstimationCode2
User-Creator Feature Polarization in Recommender Systems with Dual Influence—0
Personalized Privacy Protection Mask Against Unauthorized Facial RecognitionCode1
Predicting Star Scientists in the Field of Artificial Intelligence: A Machine Learning Approach—0
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