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Found 5 papers, 0 papers with code
Deep Causal Learning: Representation, Discovery and Inference
Causal learning has attracted much attention in recent years because causality reveals the essential relationship between things and indicates how the world progresses.
Impacts of export restrictions on the global personal protective equipment trade network during COVID-19
The threshold model captures the shortage contagion on the global PPE trade network.
Physics and Society
An interpretable neural network model through piecewise linear approximation
To address these issues, we propose a hybrid interpretable model that combines a piecewise linear component and a nonlinear component.
A hybrid machine learning framework for analyzing human decision making through learning preferences
To meet the decision maker's demand for more interpretable machine learning models, we propose a novel hybrid method, namely Neural Network-based Multiple Criteria Decision Aiding, which combines an additive value model and a fully-connected multilayer perceptron (MLP) to achieve good performance while capturing the explicit relationships between individual attributes and the prediction.
Multi-Label Annotation Aggregation in Crowdsourcing
As a means of human-based computation, crowdsourcing has been widely used to annotate large-scale unlabeled datasets.
