Abstract: Systems and methods provide for training a language model on the relationships present in a structural database. Information within a structural data is processed and converted into plain text such that the relationships within the database, such as hierarchical relationships, relations, etc. are maintained and represented in a plain text format. This information may be used as training data for a language model to provide pre-training for one or more domains. The language model may then be leveraged with natural language searching in order to identify results within a search domain response to an input query.

Abstract: Apparatuses, systems, and techniques to facilitate generation of one medical image from another medical image using one or more neural networks trained using a generative adversarial network (GAN) that utilizes a bidirectional encoder representations from transformers (BERT) as a discriminator. In at least one embodiment, one or more neural networks trained using a GAN comprising a BERT discriminator generate a positron emission tomography (PET) image from a magnetic resonance imaging (MRI) image.

Abstract: Apparatuses, systems, and techniques to generate synthesized images including digital representations of groups of cells blended realistically with appropriate background images. In at least one embodiment, background image data and gene expression data are fused together to generate such a synthesized image using one or more neural networks.