Abstract: The present disclosure relates to methods and systems that provide querying and analysis of clinical trials using probabilistic graphical models. The methods and systems train a probabilistic graphical model using clinical trial data and use the probabilistic graphical model to perform inferences in response to queries for clinical trials. The methods and systems use the probabilistic graphical model to handle multimodal datatypes of the clinical trial data and predict multiple attributes of the clinical trial for an input query.

Abstract: The disclosure relates to utilizing a domain insight system for providing plain language descriptions and insights into complex data and/or sparsely populated domains using machine-learning models and large generative models. For instance, the domain insight system converts data outputs from machine-learning models in various output formats into clear, accurate, comprehensible, and straightforward results. The domain insight system achieves this by using one or more dynamic prompts that are tailored based on the data output types and report descriptors, thus improving the accuracy and efficiency of the large generative model. In particular, the domain insight system uses specialized prompts with carefully selected parameters and, in some cases, system-level meta-prompts, to generate accurate domain-based reports and explanations for a given dataset.

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 systems, methods, and computer-readable media for utilizing a concept graphing system to determine and provide relationships between concepts within document collections or corpora. For example, the concept graphing system can generate and utilize machine-learning models, such as a sparse graph recovery machine-learning model, to identify less-obvious correlations between concepts, including positive and negative concept connections, as well as provide these connections within a visual concept graph. Additionally, the concept graphing system can provide a visual concept graph that determines and displays concept correlations based on the input of a single concept, multiple concepts, or no concepts.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for utilizing an interactive graphing system to achieve improved dataset exploration utilizing an intelligent workflow and an interactive user interface. More specifically, the interactive graphing system facilitates generating updated network graphs that include inferred user influences based on implicit user action. Indeed, the interactive graphing system can automatically generate and present a user with an updated network graph that includes added, removed, or subsetted elements and relationships that are otherwise hidden from a user. Additionally, the interactive graphing system facilitates network graph exploration and processing of customized combined network graphs that join otherwise separate network graphs.

Abstract: A method for generating fraud and approval rules for a decision engine for detecting fraud in electronic transactions includes: receiving transaction data for a plurality of electronic transactions, the transaction data including a fraud determination and data values for the respective electronic transaction; applying a first rule of a plurality of rules to the electronic transactions to identify a first subset of transactions that satisfy the first rule and include a fraud determination indicative of fraud; filtering the first subset of transactions out of the plurality of electronic transactions; repeating the application step and filtering step using additional rules of the plurality of rules until a threshold criteria is met; and generating a rule order for the first rule and the additional rules based on at least a size of the subset of transactions identified using the respective rule.

Abstract: An ensemble machine learning model is used to visualize complex data relationships. Complex data is provided to multiple generator models that each independently create a graph representing relationships in the data. At least one of the generator models is a machine learning model that can be trained with a generator model loss function. The graphs produced by the separate generator models are combined by an ensemble model to create a consensus graph. The ensemble model may be implemented as an edge-selector neural network. The models are trained jointly with a ensemble model loss function that includes the loss functions of the generator models added as regularization terms. A visualization of the ensemble graph is created to aid a user in understanding the complex data relationships.

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 methods and systems for converting Portable Document Format (PDF) documents to LaTeX files. The methods and systems use machine learning models to identify and extract PDF portions of a PDF document. The methods and systems create a LaTeX file for the PDF document using the PDF portions extracted by the machine learning models. The methods and systems provide an output with the LaTeX file for the PDF document. The LaTeX file is used to perform different actions on the PDF document.

Abstract: This disclosure relates to a time series segmentation system that efficiently and accurately segments univariate time series data. For example, the time series segmentation system utilizes proxy variable time series to identify distinct segments in a univariate time series. To illustrate, the time series segmentation system generates proxy variables that approximate a univariate time series and combine with the time series to generate a supplemented multivariate time series. The time series segmentation system then divides the supplemented multivariate time series into portions using time-based windows, converts the windowed subsequences into graph objects using a sparse graph recovery model, utilizes a conditional similarity model to determine segmentation timestamps from the graph objects, and generates a segmented univariate time series from the segmentation timestamps.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for utilizing a concept graphing system to determine and provide relationships between concepts within document collections or corpora. For example, the concept graphing system can generate and utilize machine-learning models, such as a sparse graph recovery machine-learning model, to identify less-obvious correlations between concepts, including positive and negative concept connections, as well as provide these connections within a visual concept graph. Additionally, the concept graphing system can provide a visual concept graph that determines and displays concept correlations based on the input of a single concept, multiple concepts, or no concepts.

Abstract: The present disclosure relates to methods and systems for self-teaching a large language model (LLM). The methods and systems use a self-learning framework with a plurality of phases. In each phase of the self-learning framework, the LLM generates a diverse set of outputs for a question and an aggregation is performed on the diverse set of outputs generate a phase output. The phase output from a previous phase is used as an input to the LLM in a next phase.

Abstract: The present disclosure relates to methods and systems that generate a confidence score for the generated large language model (LLM) output. The methods and systems use the text of the input provided to the LLM and the text from the generated LLM output to produce a feature vector that encodes a readability of the text from the input and the text of the LLM output. The feature vector is used to determine a corresponding confidence score for the generated LLM output. The confidence score is used to evaluate a quality of the generated LLM output.

Abstract: This disclosure relates to a real-time segmentation system that utilizes graph objects and models to efficiently and accurately generate segmented real-time time series data. The real-time segmentation system achieves this by efficiently generating new current graph objects as data points are received using a graph recovery model. Additionally, the real-time segmentation system removes previously generated graph objects beyond the current graph object and the previous graph object to reduce the amount of stored data. These object graphs can include conditional independence (CI) graphs, which are probabilistic graphical models that include nodes connected by edges to exhibit partial correlations between the nodes. Furthermore, the time series segmentation system determines segmentation timestamps from the graph objects using a similarity model.

Abstract: This disclosure relates to a time series segmentation system that automatically segments multivariate time series data. For example, the time series segmentation system is capable of converting complex and noisy multivariate time series data into segmented multivariate time series by identifying distinct segments within the data. The time series segmentation system operates with linear time complexity in terms of sequence length, which is significantly more efficient than the typical quadratic time complexity required by conventional systems. To illustrate, the time series segmentation system first divides a multivariate time series into portions using time-based windows. The time series segmentation system then converts the windowed subsequences into graph objects using a sparse graph recovery model and utilizes a similarity model to determine segmentation timestamps from the graph objects.

Abstract: Disclosed are techniques for an AI system with a large language mode (LLM) with improved accuracy and reliability in solving mathematical problems. An initial query is transformed into a template query by replacing the original input values with variables. Multiple prompts are sent to the LLM, each being different from one another, and contextually related to the template query. Multiple results are responsively received from the LLM, each result including an analytical expression to solve the mathematical problem. Each of the expressions is evaluated using a numerical evaluation tool with variables of the expression being assigned a common set of randomly sampled values. A consensus is achieved when the evaluated expressions satisfy a consensus condition, such as when all outputs match consistently over N experiments or trials. After the consensus condition is reached, the original inputs are evaluated with one or more of the expressions, and the solution is output.

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 a style-matching image generation system to generate large datasets of style-matching images having matching styles and content to an initial small sample set of input images. For example, the style-matching image generation system utilizes a selection of style-mixed stored images with a generative machine-learning model to produce large datasets of synthesized images. Further, the style-matching image generation system utilizes the generative machine-learning model to conditionally sample synthesized images that accurately match the style, content, characteristics, and patterns of the initial small sample set and that also provide added variety and diversity to the large image dataset.

Abstract: The present disclosure relates to utilizing a dynamic knowledge graph enrichment system to dynamically and automatically maintain knowledge graphs shared between groups of user identifiers with up-to-date findings and discoveries. In particular, the dynamic knowledge graph enrichment system changes static shared knowledge graphs into dynamically evolving ones utilizing statistical guarantees that automatically incorporate new edge connections into a shared knowledge graph after verifying the reliability and veracity of the proposed edge connections being offered. Further, the dynamic knowledge graph enrichment system facilitates forming new connections between different shared knowledge graphs that previously went undetected by flexibly facilitating exploration over multiple knowledge graphs and providing synergistic knowledge graph updates.