Abstract: A computer implemented method includes rendering a document page as an image; detecting tables, columns, and other associated table objects within the image via one or more table recognition models that model objects in the image as overlapping bounding boxes; transforming the set of objects into a structured representation of the table; extracting data from the objects into the structured representation; and exporting the table into the desired output format.

Abstract: A machine learning model is used to generate molecular images by text-to-image diffusion techniques based on natural language text inputs. The machine learning model is trained on combinations of molecule images and corresponding text such that representatives of both are embedded in latent space. Users provide natural language text describing molecular characteristics and the machine learning model generates an image of a molecule with those characteristics. Existing molecular images or those generated by the system can be further edited and refined with additional natural language text instructions. The system also uses machine vision techniques to understand the molecule represented by a molecular image and translate that image into other representations of the molecule.

Abstract: The present disclosure relates to utilizing an embedding space relationship query exploration system to explore embedding spaces generated by machine-learning models. For example, the embedding space relationship query exploration system facilitates efficiently and flexibly revealing relationships that are encoded in a machine-learning model during training and inferencing. In particular, the embedding space relationship query exploration system utilizes various embeddings relationship query models to explore and discover the relationship types being learned and preserved within the embedding space of a machine-learning model.

Abstract: A computer system is configured to provide an intelligent machine-learning (ML) model catalog containing data associated with multiple ML models. The multiple ML models are trained over multiple training datasets respectively, and the intelligent ML model catalog contains at least multiple training data spaces of embeddings generated based on the multiple ML models and the multiple training datasets. In response to receiving a user dataset, for at least one ML model in the plurality of ML models, the computer system is configured to extract a user data space of embeddings based on the at least one ML model and the user dataset, and evaluate the user data space against the training data space to determine whether the at least one ML model is a good fit for the user dataset.

Abstract: The monitoring of performance of a machine-learned model for use in generating an embedding space. The system uses two embedding spaces: a reference embedding space generated by applying an embedding model to reference data, and an evaluation embedding space generated by applying the embedding model to evaluation data. The system obtains multiple views of the reference embedding space, and uses those multiple views to determine a distance threshold. The system determines a distance between the evaluation and reference embedding spaces, and compares that distance with the fitness threshold. Based on the comparison, the system determines a level of acceptability of the model for use with the evaluation dataset.

Abstract: Examples of the present disclosure describe systems and methods for content-based multimedia retrieval with attention-enabled local focus. In aspects, a search query comprising multimedia content may be received by a search system. A first semantic embedding representation of the multimedia content may be generated. The first semantic embedding representation may be compared to a stored set of candidate semantic embedding representations of other multimedia content. Based on the comparison, one or more candidate representations that are visually similar to the first semantic embedding representation may be selected from the stored set of candidate semantic embedding representations. The candidate representations may be ranked, and top ‘N’ candidate representations (or corresponding multimedia items) may be retrieved and provided as search results for the search query.

Abstract: A computer implemented method includes rendering a document page as an image; detecting tables, columns, and other associated table objects within the image via one or more table recognition models that model objects in the image as overlapping bounding boxes; transforming the set of objects into a structured representation of the table; extracting data from the objects into the structured representation; and exporting the table into the desired output format.