Abstract: Systems, methods, and computer program products are provided for generating and improving multitask learning models. An example method includes determining a first accuracy metric based on at least two machine learning models performing a plurality of tasks, receiving a multitask learning model including at least one shared layer and a plurality of task-specific layers, determining a second accuracy metric based on the multitask learning model having a first number of shared layers, determining a third accuracy metric based on the multitask learning model having a second number of shared layers, comparing the accuracy metrics, repeating until at least one termination condition is satisfied, and determining a target number of shared layers for the multitask learning model based on at least one of the second accuracy metric, the third accuracy metric, the first number of shared layers, the second number of shared layers, or any combination thereof.

Abstract: Described are a method, system, and computer program product for machine learning using decoupled knowledge graphs. The method includes generating a graph including nodes connected by edges based on data of entities in a network. Generating the graph includes generating entity nodes, determining a distribution of values for an attribute of the entities, generating a lower attribute node associated with a lower subset of values for the attribute, generating a higher attribute node associated with a higher subset of values for the attribute, and generating edges connecting the nodes. The method also includes initializing node embeddings, and generating representations of the nodes by repeating, until convergence, updating the embeddings of the entity nodes while holding other embeddings static, and updating the embeddings of the non-entity nodes while holding other embeddings static. The method further includes executing a machine learning model using the representations.

Abstract: A computer-implemented method includes: receiving an inquiry request message identifying a first payment transaction having a first plurality of transaction parameters and a first authorization decision; querying a database including transaction data associated with a plurality of historical payment transactions to identify a subset of historical payment transactions, the transaction data including, for each of the plurality of historical payment transactions, a plurality of transaction parameters and an authorization decision, the subset of historical payment transactions including payment transactions having an authorization decision different from the first authorization decision and having a similarity score that satisfies a threshold; determining an impact parameter of the first plurality of transaction parameters by comparing the first plurality of transaction parameters with the plurality of transaction parameters associated with the plurality of historical payment transactions in the subset; and gener

Abstract: Provided is a system for segmenting large scale datasets according to machine learning models based on transfer learning that includes at least one processor programmed or configured to train a base machine learning model using a training dataset to generate a trained machine learning model, evaluate the trained machine learning model using an evaluation dataset, wherein, when evaluating the trained machine learning model using the evaluation dataset, the at least one processor is programmed or configured to generate a confidence score for each data instance of the evaluation dataset with the trained machine learning model, augment the evaluation dataset based on the confidence score for each data instance of the evaluation dataset to generate an augmented evaluation dataset, and retrain the trained machine learning model using the augmented evaluation dataset to generate a final machine learning model. Methods and computer program products are also provided.

Abstract: A computer-implemented method includes: receiving an inquiry request message identifying a first payment transaction having a plurality of transaction parameters and a risk score, where the risk score is generated by a machine-learning model based on the plurality of transaction parameters; for each transaction parameter of the plurality of transaction parameters, perturbing a value of the transaction parameter and re-analyzing the first payment transaction with the machine-learning model to generate a perturbed risk score based on the perturbed transaction parameter; determining at least one impact parameter from the plurality of transaction parameters by comparing the perturbed risk scores generated for each of the plurality of transaction parameters; and generating an inquiry response message based on the at least one impact parameter.

Abstract: A computer-implemented method includes: receiving an inquiry request message identifying a first payment transaction having a plurality of transaction parameters and a risk score, where the risk score is generated by a machine-learning model based on the plurality of transaction parameters; for each transaction parameter of the plurality of transaction parameters, perturbing a value of the transaction parameter and re-analyzing the first payment transaction with the machine-learning model to generate a perturbed risk score based on the perturbed transaction parameter; determining at least one impact parameter from the plurality of transaction parameters by comparing the perturbed risk scores generated for each of the plurality of transaction parameters; and generating an inquiry response message based on the at least one impact parameter.

Abstract: Systems, methods, and computer program products calculate a historical feature similarity point distribution associated with a first machine learning model, calculate a real-time feature similarity point distribution associated with a second machine learning model, and automatically provide, based on a comparison of the historical feature similarity point distribution to the real-time feature similarity point distribution, an indication of whether the second machine learning model is aligned with the first machine learning model.

Abstract: A computer-implemented method includes: receiving an inquiry request message identifying a first payment transaction having a plurality of transaction parameters and a risk score, where the risk score is generated by a machine-learning model based on the plurality of transaction parameters; for each transaction parameter of the plurality of transaction parameters, perturbing a value of the transaction parameter and re-analyzing the first payment transaction with the machine-learning model to generate a perturbed risk score based on the perturbed transaction parameter; determining at least one impact parameter from the plurality of transaction parameters by comparing the perturbed risk scores generated for each of the plurality of transaction parameters; and generating an inquiry response message based on the at least one impact parameter.

Abstract: Provided are methods for multi-task learning (MTL) in deep neural networks. An exemplary method may include receiving an MTL model; receiving a testing data set comprising testing data items for the MTL model, each testing data item comprising a plurality of elements, each element associated with a respective feature; grouping the features into a plurality of groups based on an impact of each feature on the tasks of the MTL model, determining an overall accuracy score and task-specific accuracy scores based on inputting the testing data to the MTL model; applying feature reduction evaluation (FRE) to provide a feature score for each feature; and adjusting the feature scores based on a respective grouping associated with the respective feature and at least one of the overall accuracy score, the task-specific accuracy scores, or any combination thereof to provide an adjusted feature score. Systems and computer program products are also disclosed.

Abstract: A cold cathode fluorescent lamp includes a lamp base and a light tube coupled to the lamp base. The light tube includes a light tube body having two end portions, and a getter mercury dispensing electrode sealed into each end portion. The light tube body has a relatively large diameter of about 4 mm to about 10 mm, and the getter mercury dispensing electrode has a dimension configured to fit inside the light tube body.