Abstract: A system generates prediction data by processing an input sequence segmented along a time axis based on a predetermined period using neural networks. The neural networks comprise a first neural network configured to apply dilated attention to the input sequence segmented along the time axis based on the predetermined period, and a second neural network configured to apply random partition attention to data arranged along a feature axis.

Abstract: A method of providing a specialized document sharing platform by a platform application executed by at least one processor of a terminal includes obtaining a first specialized document, generating question and answer content for the first specialized document using a question and answer language model, generating promotional content, which comprises an online promotional material for the first specialized document, based on the question and answer content, providing a promotional material production workspace based on the promotional content, determining promotional start content, which comprises the promotional content to be registered on the specialized document sharing platform, using the promotional material production workspace, and providing the promotional start content using the specialized document sharing platform.

Abstract: A learning processing device and method for pooling graph data of a hierarchical structure based on a grouping matrix, and a method for learning an artificial intelligence model. The learning processing device includes a memory and a processor in communication with the memory. The processor generates a grouping matrix of a secondary form, grouped based on a similarity of a pairwise nodes by inputting graph data into a pre-learned first artificial intelligence model; and decomposes the grouping matrix to generate a pooling matrix. The grouping matrix is decomposed in a square-root form to obtain a pooling operator.

Abstract: An autoencoding device for a synthesizable molecule generative model may include a memory configured to store chemical reaction data, chemical reaction learning information, and molecular structure information; and a processor configured to learn a neural network for a molecular generative model based on the chemical reaction data, the chemical reaction learning information, and the molecular structural information. The molecular generative model may include: a reaction sequence encoder configured to generate a latent space based on the chemical reaction learning information; a seed molecule encoder configured to generate an embedding space based on seed structural information for a seed molecule that is a final product of chemical reaction; and a decoder configured to output a reconstruction reaction sequence by decoding a reaction molecule predictive neural network and a reaction template prediction model generated from the latent space and the embedding space.

Abstract: A processing unit can determine multiple representations associated with a statement, e.g., subject or predicate representations. In some examples, the representations can lack representation of semantics of the statement. The computing device can determine a computational model of the statement based at least in part on the representations. The computing device can receive a query, e.g., via a communications interface. The computing device can determine at least one query representation, e.g., a subject, predicate, or entity representation. The computing device can then operate the model using the query representation to provide a model output. The model output can represent a relationship between the query representations and information in the model. The computing device can, e.g., transmit an indication of the model output via the communications interface.

Abstract: A processing unit can determine multiple representations associated with a statement, e.g., subject or predicate representations. In some examples, the representations can lack representation of semantics of the statement. The computing device can determine a computational model of the statement based at least in part on the representations. The computing device can receive a query, e.g., via a communications interface. The computing device can determine at least one query representation, e.g., a subject, predicate, or entity representation. The computing device can then operate the model using the query representation to provide a model output. The model output can represent a relationship between the query representations and information in the model. The computing device can, e.g., transmit an indication of the model output via the communications interface.