Abstract: A machine learning computing system for extracting structured data objects from electronic documents comprising unstructured text includes a first data repository storing a plurality of electronic documents including at least one text data object and an expert system computing device. The expert system computing device includes a processor and a non-transitory memory device storing instructions causing the expert system to receive a first data object comprising unstructured data identified from an electronic document stored in the first data repository, process, a first set of rules to identify at least one key-value pair data object from the first data object; process, by an inference engine module, a second set of rules to identify at least one free text data object from the first data object and store, in a non-transitory memory device, the at least one key-value pair and the at least one free text data object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes receiving training data; training a neural network on the training data, wherein the neural network is configured to: receive a network input, convert the network input into a latent representation of the network input, and process the latent representation to generate a network output from the network input, and wherein training the neural network on the training data comprises training the neural network on a variational information bottleneck objective that encourages, for each training input, the latent representation generated for the training input to have low mutual information with the training input while the network output generated for the training input has high mutual information with the target output for the training input.

Abstract: Various methods, apparatuses, and media for implementing a fraud machine learning model execution module are provided. A processor generates a plurality of machine learning models. The processor generates historical aggregate data based on prior transaction activities of a customer from a plurality of databases for transactions. The processor also tracks activities of the customer during a new transaction authorization process and generates a transaction data; integrates the transaction data with the historical aggregate data; executes each of said machine learning models using the integrated transaction data and the historical aggregate data to generate a fraud score and stores the fraud score into the memory; and determines whether the new transaction is fraudulent based on the generated fraud score.

Abstract: The illustrative embodiments provide a method, system, and computer program product for validating quantum algorithms using a machine learning model. In an embodiment, a method includes receiving a training data set. In an embodiment, a method includes training, by a first processor, a machine learning model with the training data set for validation of quantum circuits. In an embodiment, a method includes generating, by the machine learning model, a set of rules for validation of quantum circuits.

Abstract: An apparatus is for unsupervised domain adaptation for allowing a deep learning model with supervised learning on a source domain completed to be subjected to unsupervised domain adaptation to a target domain. The apparatus includes a first learning unit to perform a forward pass by inputting a pair (xsi, ysi) of first data xsi of the source domain and a label ysi for each of the first data and second data xTj belonging to the target domain, and insert a dropout following a Bernoulli distribution into the deep learning model in performing the forward pass, and a second learning unit to perform a back propagation to minimize uncertainty about the learning parameter of the deep learning model by using a predicted value for each class output through the forward pass and the label ysi, and an uncertainty vector for the second data xTj output through the forward pass as inputs.

Abstract: A method and system for overlaying content on a multimedia content element. The method includes: partitioning the multimedia content element into a plurality of partitions; generating at least one signature for each partition of the multimedia content element, wherein each generated signature represents a concept; determining, based on the generated at least one signature, at least one link to content; identifying, based on the generated at least one signature, at least one of the plurality of partitions as a target area of user interest; and adding, as an overlay to the multimedia content element, the determined at least one link to content, wherein the at least one link is overlaid on the at least one target area.

Abstract: One embodiment provides a method that includes determining candidate ontologies for alignment from multiple available knowledge bases. An initial target ontology is selected from the candidate ontologies and correcting the initial selected ontology with received refinement input. Concepts in the selected initial ontology are aligned with concepts of the target ontology using a deep learning hierarchical classification with received review input. A user is assisted to build, change and grow the selected initial ontology exploiting both the target ontology and new facts extracted from unstructured data.

Abstract: A first system creates and sends encryption key data to multiple data sources. A second system receives data encrypted using the encryption key data from the multiple data sources; the data may include noise data such that, even if decrypted, the original data cannot be discovered. Because the encryption is additively homomorphic, the second system may create encrypted summation data using the encrypted data. The first system separately receives the noise data encrypted using the same technique as the encrypted data. The second system may send the encrypted summation data to the first system, which may then remove the noise data from the encrypted summation data to create unencrypted summation data.

Abstract: A method of controlling computing operations in a deep neural network (DNN) is provided. A network structure of the DNN including a plurality of layers is analyzed. A hyper parameter is set based on the network structure and real-time context information of a system configured to drive the DNN. The hyper parameter is used for performing an early-stop function. Depth-wise jobs are assigned to resources included in the system based on the hyper parameter to execute the depth-wise jobs. Each of the depth-wise jobs includes at least a part of the computing operations. When an early-stop event for a first layer among the layers is generated while the plurality of depth-wise jobs are executed, a subset computing operations included in at least one second layer are performed and a remainder of the computing operations are stopped. The at least one second layer is arranged prior to the first layer.

Abstract: A method of generating an outcome for a sporting event is disclosed herein. A computing system retrieves tracking data from a data store. The computing system generates a predictive model using a deep neural network. The one or more neural networks of the deep neural network generates one or more embeddings comprising team-specific information and agent-specific information based on the tracking data. The computing system selects, from the tracking data, one or more features related to a current context of the sporting event. The computing system learns, by the deep neural network, one or more likely outcomes of one or more sporting events. The computing system receives a pre-match lineup for the sporting event. The computing system generates, via the predictive model, a likely outcome of the sporting event based on historical information of each agent for the home team, each agent for the away team, and team-specific features.

Abstract: Disclosed is a vehicular environment estimation device capable of accurately estimating a travel environment around own vehicle on the basis of a predicted route of a mobile object or the like, which is moving in a blind area. A vehicular environment estimation device that is mounted in the own vehicle detects a behavior of another vehicle in the vicinity of the own vehicle, and estimates a travel environment, which affects the traveling of another vehicle, on the basis of the behavior of another vehicle. For example, the presence of another vehicle, which is traveling in a blind area, is estimated on the basis of the behavior of another vehicle. Therefore, it is possible to estimate a vehicle travel environment that cannot be recognized by the own vehicle but can be recognized by another vehicle in the vicinity of the own vehicle.

Abstract: In one embodiment, a method includes training a baseline machine-learning model based on a neural network comprising a plurality of stages, wherein each stage comprises a plurality of neural blocks, accessing a plurality of training samples comprising a plurality of content objects, respectively, determining one or more non-local operations, wherein each non-local operation is based on one or more pairwise functions and one or more unary functions, generating one or more non-local blocks based on the plurality of training samples and the one or more non-local operations, determining a stage from the plurality of stages of the neural network, and training a non-local machine-learning model by inserting each of the one or more non-local blocks in between at least two of the plurality of neural blocks in the determined stage of the neural network.

Abstract: The present teaching relates to generating an updated model related to advertisement selection. In one example, a request is obtained for updating a model to be utilized for selecting an advertisement. A plurality of copies of the model is generated. The model is pre-selected based on a performance metric related to advertisement selection. Based on each of the plurality of copies, a candidate model is created by modifying one or more parameters of the copy of the model to create a plurality of candidate models. One of the plurality of candidate models is selected based on the performance metric. The steps of generating, creating, and selecting are repeated until a predetermined condition is met. The model is updated with the latest selected candidate model when the predetermined condition is met.

Abstract: Analyzing patterns in a volume of data and taking an action based on the analysis involves receiving data and training the data to create training examples, and then selecting features that are predictive of different classes of patterns in the data stream, using the training examples. The process further involves training in parallel a set of artificial neural networks (“ANNs”), using the data, based on the selected features, and extracting only active nodes that are representative of a class of patterns in the data stream from the set of ANNs. The process continues with adding class labels to each extracted active node, classifying patterns in the data based on the class-labeled active nodes, and taking an action based on the classifying patterns in the data.

Abstract: An information processing method for embedding watermark bits into weights of a first neural network includes: obtaining an output of a second neural network by inputting a plurality of input values obtained from a plurality of weights of the first neural network to the second neural network; obtaining second gradients of the respective plurality of input values based on an error between the output of the second neural network and the watermark bits; and updating the weights based on values obtained by adding first gradients of the weights of the first neural network that have been obtained based on backpropagation and the respective second gradients.

Abstract: A system for returning synthetic database query results. The system may include a memory unit for storing instructions, and a processor configured to execute the instructions to perform operations comprising: receiving a query input by a user at a user interface; determining, based on natural language processing, a type of the query input; determining, based on the received query input and a database language interpreter, an output data format; returning, based on a generation model and the output data format, a result of the query input; providing, to a plurality of training models and based on the determined query type, the query input and the result; and training the training models, based on the query input and the result.

Abstract: A hybrid quantum-classical (HQC) computing system, including a quantum computing component and a classical computing component, computes the inverse of a Boolean function for a given output. The HQC computing system translates a set of constraints into interactions between quantum spins; forms, from the interactions, an Ising Hamiltonian whose ground state encodes a set of states of a specific input value that are consistent with the set of constraints; performs, on the quantum computing component, a quantum optimization algorithm to generate an approximation to the ground state of the Ising Hamiltonian; and measures the approximation to the ground state of the Ising Hamiltonian, on the quantum computing component, to obtain a plurality of input bits which are a satisfying assignment of the set of constraints.

Abstract: A computer-implemented method for creating an auto-scaled predictive analytics model includes determining, via a processor, whether a queue size of a service master queue is greater than zero. Responsive to determining that the queue size is greater than zero, the processor fetches a count of requests in a plurality of requests in the service master queue and a type for each of the requests. The processor derives a value for time required for each of the requests and retrieves a number of available processing nodes based on the time required for each of the requests. The processor then auto-scales a processing node number responsive to determining that a total execution time for all of the requests in the plurality of requests exceeds a predetermined time value and outputs an auto-scaled predictive analytics model based on the processing node number and queue size.

Abstract: Methods and systems for selecting and performing group actions include selecting parameters for an approximated action-value function, which determines a reward value associated with a particular group action taken from a particular state, using a determinant of a parameter matrix for the action-value function. A group action is selected using the approximated action-value function and the selected parameters. Agents are triggered to perform respective tasks in the group action.

Abstract: A system and method predict whether a plant is abnormal by modeling a relationship equation between tags based on a correlation between the tags, applicable even if modeling is executed without understanding a target to abnormality determination, and implements internal early alarm logic based on a difference between measured data and predicted data over time. The plant abnormality prediction system includes a modeling information output unit including a pre-processing part for pre-processing past data received for a plurality of tags, a correlation analysis part for receiving the pre-processed data for each tag to determine an independent tag among the plurality of tags based on correlation coefficients for any two tags, and a modeling part for generating a relationship equation between the tags by using outputs of the pre-processing part and the correlation analysis part; and a prediction unit for calculating estimated data for the tag based on the relationship equation.