Abstract: Systems and methods for data visualization and network extraction in accordance with embodiments of the invention are illustrated. One embodiment includes a method including obtaining a graph comprising a plurality of nodes and a plurality of edges, identifying a plurality of communities in the graph, where each community includes nodes from the plurality of nodes, generating a community graph structure based on the identified communities, where the community graph includes a plurality of supernodes and a plurality of superedges, spatializing the community graph structure, unpacking the spatialized community graph structure into an unpacked graph structure comprising the plurality of nodes and the plurality of edges, where each node in the plurality of nodes is located at approximately the position of the supernode that represented it, spatializing the unpacked graph structure, and providing the spatialized unpacked graph structure.

Abstract: Disclosed are systems and methods for scenario planning by using specially programmed software engines to simulate and detect particular feature variations leading to particular outcomes based on modeling with machine learning techniques. The disclosed technology enable improved model debugging, improved simulation efficiency and accuracy, improved model explainability, improved identification of high risk or high reward scenarios, among other improvements and combinations thereof. In some embodiments, the disclosed technology implements computerized optimization techniques applied via variation generation across a dataset of test input records to optimize for feature variation along with outcome variation.

Abstract: Systems and methods for network explainability in accordance with embodiments of the invention are illustrated. In many embodiments, network structures are extracted from tabular data structures. Communities within the network structure can be identified and processed to generate rules that explain relationships in the underlying data. In various embodiments, the rules are translated into natural language for presentation to a user.

Abstract: Systems and methods for extraction of network structures from tabular data structures having numeric features are described. One embodiment includes a method of extracting a network from a tabular data structure having numerical features, comprising obtaining a tabular data structure includes several records, where each record includes several numerical values each associated with a respective numerical feature, calculating pairwise similarities between records based on the several numerical values using a distance function, generating an edge list by sorting the pairwise similarities, extracting a subset of edges from the edge list based on a connectivity threshold, constructing a network structure by generating nodes from records and connecting said nodes using edges from the subset of edges, and visualizing the network structure using a display.

Abstract: Systems and methods are disclosed for scenario planning by using specially programmed software engines to simulate and detect particular feature variations leading to particular outcomes based on modeling with machine learning techniques. The disclosed technology enables improved model debugging, improved simulation efficiency and accuracy, improved model explainability, improved identification of high risk or high reward scenarios, among other improvements and combinations thereof. Computerized optimization techniques applied via variation generation across a dataset of test input records enable optimization for feature variation along with outcome variation. Moreover, the disclosed techniques may provide and/or realize a minimized variation to input data that correspond to a point of transition from one state to another state in an outcome that results from the input data, where the transition to another state is termed a “significant” variation to the output data.

Abstract: Disclosed are systems and methods for scenario planning by using specially programmed software engines to simulate and detect particular feature variations leading to particular outcomes based on modeling with machine learning techniques. The disclosed technology enables improved model debugging, improved simulation efficiency and accuracy, improved model explainability, improved identification of high risk or high reward scenarios, among other improvements and combinations thereof. In some embodiments, the disclosed technology implements computerized optimization techniques applied via variation generation across a dataset of test input records to optimize for feature variation along with outcome variation.

Abstract: Systems and methods for data visualization and network extraction in accordance with embodiments of the invention are illustrated. One embodiment includes a method including obtaining a graph comprising a plurality of nodes and a plurality of edges, identifying a plurality of communities in the graph, where each community includes nodes from the plurality of nodes, generating a community graph structure based on the identified communities, where the community graph includes a plurality of supernodes and a plurality of superedges, spatializing the community graph structure, unpacking the spatialized community graph structure into an unpacked graph structure comprising the plurality of nodes and the plurality of edges, where each node in the plurality of nodes is located at approximately the position of the supernode that represented it, spatializing the unpacked graph structure, and providing the spatialized unpacked graph structure.

Abstract: Systems and methods for smart instance selection in accordance with embodiments of the invention are illustrated. One embodiment includes a system for selecting explanatory instances in datasets, including a processor, and a memory, the memory containing an instance selection application that configures the processor to: obtain a dataset comprising a plurality of records, obtain a machine learning model configured to classify records, initialize an explainer model, select at least one key instance from the dataset estimated to have explanatory power when provided to the explainer model, provide the explainer model with the selected at least one key instance; and provide an explanation produced by the explainer model.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Systems and methods for network explainability in accordance with embodiments of the invention are illustrated. In many embodiments, network structures are extracted from tabular data structures. Communities within the network structure can be identified and processed to generate rules that explain relationships in the underlying data. In various embodiments, the rules are translated into natural language for presentation to a user.

Abstract: Systems and methods for data visualization and network extraction in accordance with embodiments of the invention are illustrated. One embodiment includes a method including obtaining a graph comprising a plurality of nodes and a plurality of edges, identifying a plurality of communities in the graph, where each community includes nodes from the plurality of nodes, generating a community graph structure based on the identified communities, where the community graph includes a plurality of supernodes and a plurality of superedges, spatializing the community graph structure, unpacking the spatialized community graph structure into an unpacked graph structure comprising the plurality of nodes and the plurality of edges, where each node in the plurality of nodes is located at approximately the position of the supernode that represented it, spatializing the unpacked graph structure, and providing the spatialized unpacked graph structure.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Systems and methods for dataset merging using flow structures in accordance with embodiments of the invention are illustrated. Flow structures can be generated and sent to various computing devices to generate both the front-end and back-end of a customized computing system that can perform any number of various processes including those that merge datasets. In many embodiments, machine learning and/or natural language processing can be performed by the customized application.

Abstract: Systems and methods for data visualization and network extraction in accordance with embodiments of the invention are illustrated. One embodiment includes a method including obtaining a graph comprising a plurality of nodes and a plurality of edges, identifying a plurality of communities in the graph, where each community includes nodes from the plurality of nodes, generating a community graph structure based on the identified communities, where the community graph includes a plurality of supernodes and a plurality of superedges, spatializing the community graph structure, unpacking the spatialized community graph structure into an unpacked graph structure comprising the plurality of nodes and the plurality of edges, where each node in the plurality of nodes is located at approximately the position of the supernode that represented it, spatializing the unpacked graph structure, and providing the spatialized unpacked graph structure.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Systems and methods for locating telecommunication cell sites in accordance with embodiments of the invention are illustrated. One embodiment includes a method for locating cell sites, including obtaining a plurality of observations, where each observation includes a timestamp, a coordinate, an active record, and a set of passive records, uniquely identifying secondary cell sites in the passive records by cross-matching active records from a first observation with passive records from a second observation, annotating the observations with unique identifiers for each secondary cell site, time-smoothing the received signal strength values, estimating the distance from each observation to the primary cell site and secondary cell sites associated with the observation by providing a machine learning model with at least the time-smoothed signal strength values and the plurality of annotated observations, and locating the primary cell sites based on the estimated distances.