SOTAVerified

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 4551–4575 of 9051 papers

TitleStatusHype
Reinforcement Learning with Human Feedback for Realistic Traffic Simulation—0
Deformation Robust Text Spotting with Geometric Prior—0
MFR-Net: Multi-faceted Responsive Listening Head Generation via Denoising Diffusion Model—0
Is the U.S. Legal System Ready for AI's Challenges to Human Values?—0
Learning Diverse Features in Vision Transformers for Improved GeneralizationCode0
Learning Vision-based Pursuit-Evasion Robot Policies—0
Denoising Attention for Query-aware User Modeling in Personalized Search—0
Prototype Fission: Closing Set for Robust Open-set Semi-supervised Learning—0
A Comprehensive Augmentation Framework for Anomaly Detection—0
CamoFA: A Learnable Fourier-based Augmentation for Camouflage Segmentation—0
Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?—0
Ensemble of Counterfactual ExplainersCode0
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning—0
XVir: A Transformer-Based Architecture for Identifying Viral Reads from Cancer SamplesCode0
Policy Diversity for Cooperative Agents—0
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning—0
FaceCoresetNet: Differentiable Coresets for Face Set RecognitionCode0
Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series SummarizationCode0
Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-DiversityCode0
Diverse, Top-k, and Top-Quality Planning Over Simulators—0
Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messages—0
Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity ConstraintsCode0
Open Heterogeneous Data for Condition Monitoring of Multi Faults in Rotating Machines Used in Different Operating ConditionsCode0
Lexical Diversity in Kinship Across Languages and Dialects—0
On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven AnalysisCode0
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