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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 4326–4350 of 9051 papers

TitleStatusHype
Mix-ME: Quality-Diversity for Multi-Agent Learning—0
Carrier Frequency Offset Estimation for OCDM with Null Subchirps—0
Domain Randomization via Entropy Maximization—0
PRISM: Progressive Restoration for Scene Graph-based Image Manipulation—0
Data-Free Distillation of Language Model by Text-to-Text Transfer—0
Data-Centric Long-Tailed Image Recognition—0
NOD-TAMP: Generalizable Long-Horizon Planning with Neural Object Descriptors—0
Statistical Results of Multivariate Fox-H Function for Exact Performance Analysis of RIS-Assisted Wireless Communication—0
On Finding Bi-objective Pareto-optimal Fraud Prevention Rule Sets for Fintech ApplicationsCode0
Long Story Short: a Summarize-then-Search Method for Long Video Question AnsweringCode0
Task-Agnostic Low-Rank Adapters for Unseen English DialectsCode0
Artificial Intelligence Ethics Education in Cybersecurity: Challenges and Opportunities: a focus group report—0
Tailoring Mixup to Data for CalibrationCode0
Multi-dimensional data refining strategy for effective fine-tuning LLMs—0
Robustness for Free: Quality-Diversity Driven Discovery of Agile Soft Robotic Gaits—0
Optimal Budgeted Rejection Sampling for Generative Models—0
Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards—0
StableFDG: Style and Attention Based Learning for Federated Domain Generalization—0
Farthest Greedy Path Sampling for Two-shot Recommender Search—0
Evaluating Neural Language Models as Cognitive Models of Language AcquisitionCode0
Optimizing accuracy and diversity: a multi-task approach to forecast combinations—0
Diversity and Diffusion: Observations on Synthetic Image Distributions with Stable Diffusion—0
Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval—0
Role of Structural and Conformational Diversity for Machine Learning Potentials—0
A Path to Simpler Models Starts With Noise—0
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