… work, we point out that this paper deals with pre-images of general graphs, and that almost
no use is made of the special properties of molecular graphs. As an important example, the …

… pre-images: kPCA and KDE. Then, in Section 3 we describe our approach for learning
pre-images… include the inference of pre-images in structures like graphs and incorporating a-priori …

… pre-images: kPCA and KDE. Then, in Section 3 we describe our approach for learning
pre-images… include the inference of pre-images in structures like graphs and incorporating a-priori …

… construct graph pre-images given a graph kernel. First, we provide an in-depth review of graph
… -art graph edit distance heuristics and propose a metric learning approach to estimate the …

… graph pre-images as median graphs, by aligning graph edit distances (GEDs) in the graph
space with distances in the graph … In other words, we seek to learn the edit costs of the GED, …

… An inherent advantage of learning reasons is learning with few samples. Causality gained …
sive model, and estimating pre-images to obtain topology of the graph in the input space. The …

… However, finding pre-images is often a difficult optimization problem. This paper attempts
to use evolution strategies (ES) to seek pre-images. This method … Learning to find pre-images …

… pre-images, as well as the sampled quantitative values y and the predicted value f(G) using
our learned … demonstrates that our model can generate meaningful pre-images of points in \({\…

… in the feature space, then finds the pre-images in the input space to infer the … preimages
were used to infer the directional network topology in the original space by adopting the learning …

… concentrating them at a point or a curve. We refer to this as … shown to be very effective in
learning such models from data, … algorithm automatically adapts the learning rate of individual …