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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
Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language ModelsCode0
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation—0
Interpretable Learning Dynamics in Unsupervised Reinforcement Learning—0
Call for Action: towards the next generation of symbolic regression benchmark—0
Accelerating Evolution: Integrating PSO Principles into Real-Coded Genetic Algorithm Crossover—0
Pinching Antenna-enabled ISAC Systems: Exploiting Look-Angle Dependence of RCS for Target Diversity—0
Exploring the Role of Diversity in Example Selection for In-Context LearningCode0
Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational VideosCode0
Synthesize-on-Graph: Knowledgeable Synthetic Data Generation for Continue Pre-training of Large Language Models—0
Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis—0
Subset Selection for Fine-Tuning: A Utility-Diversity Balanced Approach for Mathematical Domain Adaptation—0
Fluctuating growth rates link turnover and unevenness in species-rich communities—0
Pixel3DMM: Versatile Screen-Space Priors for Single-Image 3D Face Reconstruction—0
Enriching the Korean Learner Corpus with Multi-reference Annotations and Rubric-Based Scoring—0
Rethinking Time Encoding via Learnable Transformation FunctionsCode0
Cooking Up Creativity: A Cognitively-Inspired Approach for Enhancing LLM Creativity through Structured RepresentationsCode0
Information Gravity: A Field-Theoretic Model for Token Selection in Large Language Models—0
JaccDiv: A Metric and Benchmark for Quantifying Diversity of Generated Marketing Text in the Music Industry—0
Clustering-Based Evolutionary Federated Multiobjective Optimization and Learning—0
Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts—0
FedAvgen: Metadata for Model Aggregation In Communication Systems—0
Hierarchical Uncertainty-Aware Graph Neural Network—0
Accurate and Diverse LLM Mathematical Reasoning via Automated PRM-Guided GFlowNets—0
Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments—0
CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe GenerationCode0
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