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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 1226–1250 of 9051 papers

TitleStatusHype
SEE-DPO: Self Entropy Enhanced Direct Preference Optimization—0
Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment ApproachCode0
Evolutionary features in a minimal physical system: directionality, diversity, selection, growth, inheritance, and adaptation—0
Community Forensics: Using Thousands of Generators to Train Fake Image DetectorsCode1
How Does A Text Preprocessing Pipeline Affect Ontology Syntactic Matching?Code0
Hiring as Exploration—0
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
No Culture Left Behind: ArtELingo-28, a Benchmark of WikiArt with Captions in 28 LanguagesCode0
Exploring the Potentials and Challenges of Using Large Language Models for the Analysis of Transcriptional Regulation of Long Non-coding RNAs—0
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training—0
SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-AgentsCode1
A Post-Training Enhanced Optimization Approach for Small Language Models—0
IMUDiffusion: A Diffusion Model for Multivariate Time Series Synthetisation for Inertial Motion Capturing Systems—0
Conditional Vendi Score: An Information-Theoretic Approach to Diversity Evaluation of Prompt-based Generative ModelsCode0
Spontaneous Emergence of Agent Individuality through Social Interactions in LLM-Based Communities—0
Growing a Tail: Increasing Output Diversity in Large Language Models—0
Towards Leveraging News Media to Support Impact Assessment of AI Technologies—0
Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language AttackCode1
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives—0
Not Just Object, But State: Compositional Incremental Learning without ForgettingCode1
Learning General-Purpose Biomedical Volume Representations using Randomized SynthesisCode2
Evaluating Creative Short Story Generation in Humans and Large Language ModelsCode0
A Coverage-Guided Testing Framework for Quantum Neural Networks—0
Diversity Progress for Goal Selection in Discriminability-Motivated RL—0
Diversidade linguística e inclusão digital: desafios para uma ia brasileira—0
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