📄️ Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks
Multiple Instance Learning (MIL) is a weakly supervised learning problem where the aim is to assign labels to sets or bags of instances, as opposed to traditional supervised learning where each instance is assumed to be independent and identically distributed (i.i.d.) and is to be labeled individually.
📄️ Random Graphs for Bayesian Graph Neural Networks
Real-world data is noisy. Graphs are constructed from data -> Observed graphs can contain errors.
📄️ GEEM : Active Learning for Graphs
Some nodes are more informative than others.
📄️ RNN with Particle Flow for Probabilistic Spatio-Temporal Forecasting
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks.
📄️ Microwave Breast Cancer Detection
Early detection of breast cancer significantly increases the chance of recovery.