ADVERSARIAL AUTOENCODER ARCHITECTURE FOR METHODS OF GRAPH TO SEQUENCE MODELS

A graph-to-sequence (G2S) architecture is configured to use graph data of objects to generate sequence data of new objects. The process can be used with objects types that can be represented as graph data and sequence data. For instance, such data is molecular data, where each molecule can be represented as molecular graph and in SMILES. Examples also include popular tasks in deep learning of image-to-text or/and image-to-speech translations. Images can be naturally represented as graphs, while text and speech can be natively represented as sequences. The G2S architecture can include a graph encoder and sample generator that produce latent data in a latent space, which latent data can be conditioned with properties of the object. The latent data is input into a discriminator to obtain real or fake objects, and input into a decoder for generating the sequence data of the new objects.