Abstract: The present disclosure relates to methods and systems that provide a federated learning framework using Neural Graphical Models. The federated learning framework combines the individual distributions learned by each client into a global model while keeping the data of each client private within each client's environment. The methods and systems allow for knowledge sharing among the clients without data sharing.

Abstract: The present disclosure relates to a dataset exploration system based on input data having a plurality of data samples having a plurality of features. In particular, the systems described herein generate preprocessed input data including one or more of performing data normalization, calculating covariance matrix, and assessing data quality of the preprocessed input data. The system further generates a domain structure from the preprocessed input data. The system further includes recovering a probabilistic graphical model (PGM) trained to discover the underlying joint distribution over the plurality of features based on the preprocessed input data and the domain structure. The learned PGM may be utilized to answer user queries by leveraging its probabilistic inference capabilities on the data and various different visual outputs may be presented via a display device.

Abstract: The present disclosure relates to propagating knowledge between nodes of a feature graph in inferring or otherwise predicting attribute values for various features represented within the feature graph. This enables analysis of the feature graph beyond direct dependencies and for domain spaces that are increasingly complex. The present disclosure includes generating a transition matrix based on correlations within the feature graph to determine distribution of weights to apply to an attribute matrix including a combination of known and unknown attribute values. Features described herein provide a computationally inexpensive and flexible approach to evaluating graphs of complex domains while considering combinations of features that are not necessarily directly correlated to other features.

Abstract: The present disclosure relates to recovering a sparse feature graph based on input data having a collection of samples and associated features. In particular, the systems described herein utilize a fully connected neural network to learn a regression of the input data and determine direct connections between features of the input data while the neural network satisfies one or more sparsity constraints. This regression may be used to recover a feature graph indicating direct connections between the features of the input data. In addition, the feature graph may be presented via an interactive presentation that enables a user to navigate nodes and edges of the graph to gain insights of the input data and associated features.

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: 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.