Nature

Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting

We investigate the potential of graph neural networks (GNNs) for transfer learning and improved molecular property prediction in the context of funnels or screening cascades characteristic of drug ...
Electronic Design

Software Makes Transfer Functions More Manageable

Small-signal analysis of switching dc-dc converters determines their specific transfer functions, for example, for stability analysis or design of a proper input filter. Transfer functions of interest ...
Nature

Coronary heart disease prediction method fusing domain-adaptive transfer learning with graph convolutional networks (GCN)

Graph convolutional networks (GCNs) have achieved impressive results in many medical scenarios involving graph node classification tasks. However, there are difficulties in transfer learning for graph ...
Electronic Design

Applying the Laplace Transform in LTspice to Model Transfer Functions

What is a transfer function? How to implement a Laplace transform in LTspice. Analyzing transfer functions in the frequency and time domains. Looking at compensator design in LTspice. Transfer ...