Abstract: The present disclosure relates to methods and systems for providing a neural graphical model. The methods and systems generate a neural view of the neural graphical model for a domain. The input data is generated from the domain and includes generic input data. The input data also includes a combination of different data types of input data. The neural view of the neural graphical model represents the functions of the different features of the domain using a neural network. The functions are learned for the features of the domain using a dependency structure of an input graph for the input data and the neural network. The methods and systems use the neural graphical model to perform inference tasks. The methods and systems also use the neural graphical model to perform sampling tasks.

Abstract: The present disclosure relates to methods and systems for providing a neural graphical model. The methods and systems generate a neural view of the neural graphical model for input data. The neural view of the neural graphical model represents the functions of the different features of the domain using a neural network. The functions are learned for the features of the domain using a dependency structure of an input graph for the input data using neural network training for the neural view. The methods and systems use the neural graphical model to perform inference tasks. The methods and systems also use the neural graphical model to perform sampling tasks.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for utilizing a network graph exploration system to facilitate improved exploration of network graphs via inferencing and improved visualization. For example, the network graph exploration system utilizes inferencing to accurately facilitate question and answer explorations, impute missing data in data sets, and perform accurate evaluations. Additionally, in various implementations, the network graph exploration system compresses large, busy, complex, and unreadable network graphs into smaller structures that offer better readability while preserving the primary properties and structure of the domain of a network graph.

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: 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: A computer implemented method includes obtaining deep learning model embedding for each instance present in a dataset, the embedding incorporating a measure of concept similarity. An identifier of a first instance of the dataset is received. A similarity distance is determined based on the respective embeddings of the first instance and a second instance. Similarity distances between embeddings, represented as points, imply a graph, where each instance's embedding is connected by an edge to a set of similar instances' embeddings. Sequences of connected points, referred to as walks, provide valuable information about the dataset and the deep learning model.