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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 2476–2500 of 9051 papers

TitleStatusHype
Data-driven Discovery of Biophysical T Cell Receptor Co-specificity Rules—0
Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case StudyCode0
Towards Effective Graph Rationalization via Boosting Environment Diversity—0
S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical ImagingCode0
Theoretical Analysis of Quality Diversity Algorithms for a Classical Path Planning Problem—0
AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.0—0
Personalized LLM for Generating Customized Responses to the Same Query from Different UsersCode0
Error Diversity Matters: An Error-Resistant Ensemble Method for Unsupervised Dependency ParsingCode0
Experimental Study on the Effect of Synchronization Accuracy for Near-Field RF Wireless Power Transfer in Multi-Antenna Systems—0
Classification Drives Geographic Bias in Street Scene Segmentation—0
Decoding OTC Government Bond Market Liquidity: An ABM Model for Market Dynamics—0
Binary or nonbinary? An evolutionary learning approach to gender identity—0
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and RegressionCode0
StyleDiT: A Unified Framework for Diverse Child and Partner Faces Synthesis with Style Latent Diffusion Transformer—0
The Language of Motion: Unifying Verbal and Non-verbal Language of 3D Human Motion—0
Benchmarking Linguistic Diversity of Large Language ModelsCode0
One world, one opinion? The superstar effect in LLM responses—0
First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI—0
CSSDH: An Ontology for Social Determinants of Health to Operational Continuity of Care Data Interoperability—0
Learning Camera Movement Control from Real-World Drone Videos—0
STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading—0
Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks—0
eCARLA-scenes: A synthetically generated dataset for event-based optical flow predictionCode0
Dialogue Language Model with Large-Scale Persona Data Engineering—0
Benchmarking LLMs for Mimicking Child-Caregiver Language in Interaction—0
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