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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 4851–4875 of 9051 papers

TitleStatusHype
Quantity versus Diversity: Influence of Data on Detecting EEG Pathology with Advanced ML Models—0
Quantum-Inspired Evolutionary Algorithms for Feature Subset Selection: A Comprehensive Survey—0
Quasi-random Multi-Sample Inference for Large Language Models—0
Query Completion Using Bandits for Engines Aggregation—0
Query Expansion Using Contextual Clue Sampling with Language Models—0
Query-Focused Opinion Summarization for User-Generated Content—0
Question-Answering Based Summarization of Electronic Health Records using Retrieval Augmented Generation—0
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
RAGSys: Item-Cold-Start Recommender as RAG System—0
RAILS: A Robust Adversarial Immune-inspired Learning System—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
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
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