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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 8076–8100 of 9051 papers

TitleStatusHype
Guylingo: The Republic of Guyana Creole CorporaCode0
Dominated Novelty Search: Rethinking Local Competition in Quality-DiversityCode0
DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited DataCode0
Long-term Data Sharing under Exclusivity AttacksCode0
Unifying gradient regularization for Heterogeneous Graph Neural NetworksCode0
The ADUULM-360 Dataset -- A Multi-Modal Dataset for Depth Estimation in Adverse WeatherCode0
GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trickCode0
Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation NetworksCode0
LookHere: Vision Transformers with Directed Attention Generalize and ExtrapolateCode0
The Algonauts Project 2023 Challenge: UARK-UAlbany Team SolutionCode0
Looking for a Handsome Carpenter! Debiasing GPT-3 Job AdvertisementsCode0
Confidence-weighted integration of human and machine judgments for superior decision-makingCode0
Conditional Vendi Score: An Information-Theoretic Approach to Diversity Evaluation of Prompt-based Generative ModelsCode0
Present and Future Generalization of Synthetic Image DetectorsCode0
Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI SystemsCode0
Preserving Fine-Grain Feature Information in Classification via Entropic RegularizationCode0
Unifying Human and Statistical Evaluation for Natural Language GenerationCode0
Simple 2D Convolutional Neural Network-based Approach for COVID-19 DetectionCode0
Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-EncodersCode0
Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization FunctionCode0
Conditional Quantile Estimation for Uncertain Watch Time in Short-Video RecommendationCode0
Guiding and Diversifying LLM-Based Story Generation via Answer Set ProgrammingCode0
Does Writing with Language Models Reduce Content Diversity?Code0
Simple Noisy Environment Augmentation for Reinforcement LearningCode0
Words with Consistent Diachronic Usage Patterns are Learned Earlier: A Computational Analysis Using Temporally Aligned Word EmbeddingsCode0
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