Search Results for author:
Found 5 papers, 3 papers with code
Edge-Wise Graph-Instructed Neural Networks
The problem of multi-task regression over graph nodes has been recently approached through Graph-Instructed Neural Network (GINN), which is a promising architecture belonging to the subset of message-passing graph neural networks.
GradINN: Gradient Informed Neural Network
We propose Gradient Informed Neural Networks (GradINNs), a methodology inspired by Physics Informed Neural Networks (PINNs) that can be used to efficiently approximate a wide range of physical systems for which the underlying governing equations are completely unknown or cannot be defined, a condition that is often met in complex engineering problems.
Sparse Implementation of Versatile Graph-Informed Layers
Recently, Graph-Informed (GI) layers were introduced to address regression tasks on graph nodes, extending their applicability beyond classic GNNs.
Graph-Informed Neural Networks for Sparse Grid-Based Discontinuity Detectors
In this paper, we present a novel approach for detecting the discontinuity interfaces of a discontinuous function.
Alternate Training of Shared and Task-Specific Parameters for Multi-Task Neural Networks
The proposed alternate training method updates shared and task-specific weights alternately, exploiting the multi-head architecture of the model.
