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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 6851–6900 of 9051 papers

TitleStatusHype
QuPeL: Quantized Personalization with Applications to Federated Learning—0
QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Engines—0
R2-Talker: Realistic Real-Time Talking Head Synthesis with Hash Grid Landmarks Encoding and Progressive Multilayer Conditioning—0
R3 Adversarial Network for Cross Model Face Recognition—0
RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations—0
An Instance Space Analysis of Constrained Multi-Objective Optimization Problems—0
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction—0
RAGSys: Item-Cold-Start Recommender as RAG System—0
RAILS: A Robust Adversarial Immune-inspired Learning System—0
An Inclusive Foundation Model for Generalizable Cytogenetics in Precision Oncology—0
An Improved Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization—0
A novel multiobjective evolutionary algorithm based on decomposition and multi-reference points strategy—0
An Improved LSHADE-RSP Algorithm with the Cauchy Perturbation: iLSHADE-RSP—0
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts—0
Rainy screens: Collecting rainy datasets, indoors—0
RandMSAugment: A Mixed-Sample Augmentation for Limited-Data Scenarios—0
Random Features for Compositional Kernels—0
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures—0
Randomized heuristic repair for large-scale multidimensional knapsack problem—0
RBUE: A ReLU-Based Uncertainty Estimation Method of Deep Neural Networks—0
Randomized Spline Trees for Functional Data Classification: Theory and Application to Environmental Time Series—0
RandoMix: A mixed sample data augmentation method with multiple mixed modes—0
Abnormal Event Detection In Videos Using Deep Embedding—0
Random Network Distillation as a Diversity Metric for Both Image and Text Generation—0
RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data—0
Unsupervised Fine-Tuning Data Selection for ASR Using Self-Supervised Speech Models—0
RankAug: Augmented data ranking for text classification—0
Rank diversity of languages: Generic behavior in computational linguistics—0
Ranking-aware Uncertainty for Text-guided Image Retrieval—0
Unsupervised Hierarchical Story Infilling—0
Ranking in Genealogy: Search Results Fusion at Ancestry—0
A blindspot of AI ethics: anti-fragility in statistical prediction—0
Ranking with social cues: Integrating online review scores and popularity information—0
Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event—0
RAP-Net: Recurrent Attention Pooling Networks for Dialogue Response Selection—0
Unsupervised inference approach to facial attractiveness—0
Rare recombination events generate sequence diversity among balancer chromosomes in Drosophila melanogaster—0
An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction—0
RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications—0
Unsupervised Learning of Compositional Scene Representations from Multiple Unspecified Viewpoints—0
RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow Trajectories—0
R-BI: Regularized Batched Inputs enhance Incremental Decoding Framework for Low-Latency Simultaneous Speech Translation—0
An Improved Dung Beetle Optimizer for Random Forest Optimization—0
RD-DPP: Rate-Distortion Theory Meets Determinantal Point Process to Diversify Learning Data Samples—0
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation—0
Unsupervised Learning of Depth Estimation and Visual Odometry for Sparse Light Field Cameras—0
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning—0
Reading Recognition in the Wild—0
Reading Yule in light of the history and present of macroevolution—0
Wide Aspect Ratio Matching for Robust Face Detection—0
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