Techniques for automatic intelligent information extraction from an electronic document are disclosed. In one embodiment, a computerized method is disclosed comprising training a label prediction model to generate a set of label predictions, obtaining an electronic document, analyzing the electronic document and determining a set of features for each of a set of information items identified in the electronic document, obtaining model output from the label prediction model for each information item, the model output comprising, for a respective information item, a set of probabilities corresponding to a set of information classes, and generating an information extraction comprising a set of labels corresponding to the set of information items.

A method trains and generates a matrix solving approach library package for optimizing a matrix solving application. A computer system with implemented the method may 1) receive requests to train a matrix solving Machine Learning (ML) model; 2) design a model structure of the ML Model accordingly; 3) select a set of matrices solving sampling data for training the defined matrix solving ML model; 4) use the selected matrix solving data and constructed IMSA Structure as inputs to train the matrix solving ML model; 5) generates a new matrix solving ML model with optimized IMSA parameters as ML model outputs; optimize the weights for each ML model node according to the provided training data sets; 6) verify the trained matrix solving approach library package with untrained data sets (matrix solving problems). The trained matrix solving approach library package may optimize matric solving application for solving matrix with result.