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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 601–625 of 9051 papers

TitleStatusHype
DUAL: Diversity and Uncertainty Active Learning for Text SummarizationCode0
How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code—0
AILS-NTUA at SemEval-2025 Task 8: Language-to-Code prompting and Error Fixing for Tabular Question AnsweringCode0
Jointly Understand Your Command and Intention:Reciprocal Co-Evolution between Scene-Aware 3D Human Motion Synthesis and Analysis—0
Embracing Diversity: A Multi-Perspective Approach with Soft Labels—0
Autoencoder-Based Framework to Capture Vocabulary Quality in NLP—0
Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform—0
Modeling cell differentiation in neuroblastoma: insights into development, malignancy, and treatment relapse—0
Innovation-exnovation dynamics on trees and trusses—0
ADAGE: Active Defenses Against GNN Extraction—0
LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces—0
Recent Advances on Generalizable Diffusion-generated Image DetectionCode1
RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering—0
Identifiable Multi-View Causal Discovery Without Non-Gaussianity—0
UIFace: Unleashing Inherent Model Capabilities to Enhance Intra-Class Diversity in Synthetic Face Recognition—0
Revisiting Self-Consistency from Dynamic Distributional Alignment Perspective on Answer Aggregation—0
DiffCSS: Diverse and Expressive Conversational Speech Synthesis with Diffusion Models—0
Where Are We? Evaluating LLM Performance on African Languages—0
Low-Confidence Gold: Refining Low-Confidence Samples for Efficient Instruction Tuning—0
IndicEval-XL: Bridging Linguistic Diversity in Code Generation Across Indic LanguagesCode0
Distill Any Depth: Distillation Creates a Stronger Monocular Depth EstimatorCode4
EnDive: A Cross-Dialect Benchmark for Fairness and Performance in Large Language Models—0
Effect of Gender Fair Job Description on Generative AI Images—0
SMT(LIA) Sampling with High Diversity—0
MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment—0
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