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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 7076–7100 of 9051 papers

TitleStatusHype
An Empirical Study of Diversity of Word Alignment and its Symmetrization Techniques for System Combination—0
An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research—0
Rethinking Diversity in Deep Neural Network Testing—0
Rethinking and Refining the Distinct Metric—0
UrduLLaMA 1.0: Dataset Curation, Preprocessing, and Evaluation in Low-Resource Settings—0
URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement—0
An Empirical Comparison of Sampling Quality Metrics: A Case Study for Bayesian Nonnegative Matrix Factorization—0
Rethinking Data Selection for Supervised Fine-Tuning—0
AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications—0
Rethinking Ecological Measures Of Functional Diversity—0
An Empirical Bayes Framework for Open-Domain Dialogue Generation—0
An Empirical Analysis of Diversity in Argument Summarization—0
An Efficient Genetic Algorithm for Discovering Diverse-Frequent Patterns—0
An Efficient Asynchronous Batch Bayesian Optimization Approach for Analog Circuit Synthesis—0
Rethinking Image Cropping: Exploring Diverse Compositions From Global Views—0
An Educational System for Personalized Teacher Recommendation in K-12 Online Classrooms—0
An Edge Alignment-based Orientation Selection Method for Neutron Tomography—0
Rethinking LLM-Based Recommendations: A Query Generation-Based, Training-Free Approach—0
Anchor-Controlled Generative Adversarial Network for High-Fidelity Electromagnetic and Structurally Diverse Metasurface Design—0
Use of Genome Information-Based Potentials to Characterize Human Adaptation—0
Rethinking Parameter Counting: Effective Dimensionality Revisited—0
Ancestral Reinforcement Learning: Unifying Zeroth-Order Optimization and Genetic Algorithms for Reinforcement Learning—0
Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation—0
Use of Speech Impairment Severity for Dysarthric Speech Recognition—0
RETHINKING SELF-DRIVING : MULTI -TASK KNOWLEDGE FOR BETTER GENERALIZATION AND ACCIDENT EXPLANATION ABILITY—0
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