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Graph-Aware Transformer: Is Attention All Graphs Need?
To cover the broad range of graph-data applications including graph classification as well as graph generation, it is desirable to have a general and flexible model consisting of an encoder and a decoder that can handle graph data.
LATENT OPTIMIZATION VARIATIONAL AUTOENCODER FOR CONDITIONAL MOLECULAR GENERATION
Variational autoencoder (VAE) is a generation algorithm, consisting of an encoder and a decoder, and the latent variable from the encoder is used as the input of the decoder.
Learning Monotonic Alignments with Source-Aware GMM Attention
Transformers with soft attention have been widely adopted to various sequence-to-sequence tasks.
Image-to-Graph Transformers for Chemical Structure Recognition
For several decades, chemical knowledge has been published in written text, and there have been many attempts to make it accessible, for example, by transforming such natural language text to a structured format.
