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Found 5 papers, 2 papers with code
CML: A Contrastive Meta Learning Method to Estimate Human Label Confidence Scores and Reduce Data Collection Cost
In this paper, we propose a contrastive meta-learning framework (CML) to address the challenges introduced by noisy annotated data, specifically in the context of natural language processing.
Generalizable Error Modeling for Search Relevance Data Annotation Tasks
This paper presents a predictive error model trained to detect potential errors in search relevance annotation tasks for three industry-scale ML applications (music streaming, video streaming, and mobile apps) and assesses its potential to enhance the quality and efficiency of the data annotation process.
Visiting Distant Neighbors in Graph Convolutional Networks
We extend the graph convolutional network method for deep learning on graph data to higher order in terms of neighboring nodes.
Social distancing in pedestrian dynamics and its effect on disease spreading
For the mobility dynamics, we design an agent based model consisting of pedestrian dynamics with a novel type of force to resemble social distancing in crowded sites.
Physics and Society Populations and Evolution
A transfer learning metamodel using artificial neural networks applied to natural convection flows in enclosures
We adopted two approaches to this problem: Firstly, we made use of a multi-grid dataset in order to train our artificial neural network in a cost-effective manner.
