Abstract: An information processing method includes acquiring first output data for input data of first learning model, reference data for the input data, and second output data for the input data of second learning model obtained by converting first learning model; calculating first difference data corresponding to a difference between the first difference data and the reference data and second difference data corresponding to a difference between the second output data and the reference data; and training first learning model with use of the first difference data and the second difference data.

Abstract: A method of generating a question-answer learning model through adversarial learning may include: sampling a latent variable based on constraints in an input passage; generating an answer based on the latent variable; generating a question based on the answer; and machine-learning the question-answer learning model using a dataset of the generated question and answer, wherein the constraints are controlled so that the latent variable is present in a data manifold while increasing a loss of the question-answer learning model.

Abstract: A first and second blending profile may be created for a set of question answering pipelines. A set of test answer data may be generated for a first answering pipeline. The test answer data may be generated based on a set of test question and using an answer key associated with the test questions. Based on the test answer data, a first blending profile can be created for the first answering pipeline. Using the set of test questions and a second answer key, another set of test answer data may be generated. This set may be generated for the second answering pipeline. Using this second answering pipeline test answer data, a second blending profile can be generated for the second answering pipeline. Each blending profile may have metadata about a confidence of each pipeline.

Abstract: Digital content interaction prediction and training techniques that address imbalanced classes are described. In one or more implementations, a digital medium environment is described to predict user interaction with digital content that addresses an imbalance of numbers included in first and second classes in training data used to train a model using machine learning. The training data is received that describes the first class and the second class. A model is trained using machine learning. The training includes sampling the training data to include at least one subset of the training data from the first class and at least one subset of the training data from the second class. Iterative selections are made of a batch from the sampled training data. The iteratively selected batches are iteratively processed by a classifier implemented using machine learning to train the model.

Abstract: An interaction prediction system for accurately predicting the occurrence of interactions, entities associated with the interactions, and/or resources involved with the interactions. The interaction predictions can be used for a number of different purposes, such as improving security of systems, predicting future interactions or the likelihood thereof, or the like. The interaction prediction system described herein more accurately predict the interactions using modeling and monitoring that increases the processing speeds by reducing the data needed to make the predictions, reduces the memory requirements to make the predictions, and increases the capacity of the processing systems when compared to traditional systems.

Abstract: An intelligent system, such as an autonomous robot agent, includes systems and methods to learn various aspects about a task in response to instructions received from a human instructor, to apply the instructed knowledge immediately during task performance following the instruction, and to instruct other intelligent systems about the knowledge for performing the task. The learning is accomplished free of training the intelligent system. The instructions from the human instructor may be provided in a natural language format and may include deictic references. The instructions may be received while the intelligent system is online, and may be provided to the intelligent system in one shot, e.g., in a single encounter or transaction with the human instructor.

Abstract: Systems for distributed, event-based computation are provided. In various embodiments, the systems include a plurality of neurosynaptic processors and a network interconnecting the plurality of neurosynaptic processors. Each neurosynaptic processor includes a clock uncoupled from the clock of each other neurosynaptic processor. Each neurosynaptic processor is adapted to receive an input stream, the input stream comprising a plurality of inputs and a clock value associated with each of the plurality of inputs. Each neurosynaptic processor is adapted to compute, for each clock value, an output based on the inputs associated with that clock value. Each neurosynaptic processor is adapted to send to another of the plurality of neurosynaptic processors, via the network, the output and an associated clock value.

Abstract: Generating a solution keyword tag cloud is provided. The solution keyword tag cloud is generated for a product based on matching keywords identified in a question asking how to resolve an issue experienced by a user with the product with keyword tags included in a set of condition-solution trees corresponding to the product. In response to receiving an indication that a tried solution in the solution keyword tag cloud did not resolve the issue experienced by the user, the solution keyword tag cloud is graphically updated by moving the tried solution that failed to resolve the issue from a solution section of the solution keyword tag cloud to a condition section of the solution keyword tag cloud. The solution keyword tag cloud is presented in a graphical user interface display on a client device corresponding to the user.

Abstract: Methods, systems, and computer program products for determining data representative of bias within a model are provided herein. A computer-implemented method includes obtaining a first dataset on which a model was trained, wherein the first dataset contains protected attributes, and a second dataset on which the model was trained, wherein the protected attributes have been removed from the second dataset; identifying, for each of the one or more protected attributes in the first dataset, one or more attributes in the second dataset correlated therewith; determining bias among at least a portion of the identified correlated attributes; and outputting, to at least one user, identifying information pertaining to the one or more instances of bias.

Abstract: In one embodiment, a device distributes sets of training records from a training dataset for a random forest-based classifier among a plurality of workers of a computing cluster. Each worker determines whether it can perform a node split operation locally on the random forest by comparing a number of training records at the worker to a predefined threshold. The device determines, for each of the split operations, a data size and entropy measure of the training records to be used for the split operation. The device applies a machine learning-based predictor to the determined data size and entropy measure of the training records to be used for the split operation, to predict its completion time. The device coordinates the workers of the computing cluster to perform the node split operations in parallel such that the node split operations in a given batch are grouped based on their predicted completion times.

Abstract: Computer network architectures for machine learning, and more specifically, computer network architectures for the automated completion of healthcare claims. Embodiments of the present invention provide computer network architectures for the automated completion of estimated final cost data for claims for healthcare clinical episodes using incomplete data for healthcare insurance claims and costs, known to date. Embodiments may use an automatic claims completion web application, with other computer network architecture components. Embodiments may include a combination of third-party databases to generate estimated final claims for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, social-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: A micro-processor circuit and a method of performing neural network operation are provided. The micro-processor circuit is suitable for performing neural network operation. The micro-processor circuit includes a parameter generation module, a compute module and a truncation logic. The parameter generation module receives in parallel a plurality of input parameters and a plurality of weight parameters of the neural network operation. The parameter generation module generates in parallel a plurality of sub-output parameters according to the input parameters and the weight parameters. The compute module receives in parallel the sub-output parameters. The compute module sums the sub-output parameters to generate a summed parameter. The truncation logic receives the summed parameter. The truncation logic performs a truncation operation based on the summed parameter to generate a plurality of output parameters of the neural network operation.

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that asynchronously train a machine learning model across client devices that implement local versions of the model while preserving client data privacy. To train the model across devices, in some embodiments, the disclosed systems send global parameters for a global machine learning model from a server device to client devices. A subset of the client devices uses local machine learning models corresponding to the global model and client training data to modify the global parameters. Based on those modifications, the subset of client devices sends modified parameter indicators to the server device for the server device to use in adjusting the global parameters. By utilizing the modified parameter indicators (and not client training data), in certain implementations, the disclosed systems accurately train a machine learning model without exposing training data from the client device.

Abstract: Methods and systems for accelerated training of a machine learning based model for semiconductor applications are provided. One method for training a machine learning based model includes acquiring information for non-nominal instances of specimen(s) on which a process is performed. The machine learning based model is configured for performing simulation(s) for the specimens. The machine learning based model is trained with only information for nominal instances of additional specimen(s). The method also includes re-training the machine learning based model with the information for the non-nominal instances of the specimen(s) thereby performing transfer learning of the information for the non-nominal instances of the specimen(s) to the machine learning based model.

Abstract: Methods, systems, and apparatus for solving computational tasks using quantum computing resources. In one aspect a method includes receiving, at a quantum formulation solver, data representing a computational task to be performed; deriving, by the quantum formulation solver, a formulation of the data representing the computational task that is formulated for a selected type of quantum computing resource; routing, by the quantum formulation solver, the formulation of the data representing the computational task to a quantum computing resource of the selected type to obtain data representing a solution to the computational task; generating, at the quantum formulation solver, output data including data representing a solution to the computational task; and receiving, at a broker, the output data and generating one or more actions to be taken based on the output data.

Abstract: Machine learning classification models which are robust against label noise are provided. Noise may be modelled explicitly by modelling “label flips”, where incorrect binary labels are “flipped” relative to their ground truth value. Distributions of label flips may be modelled as prior and posterior distributions in a flexible architecture for machine learning systems. An arbitrary classification model may be provided within the system. The classification model is made more robust to label noise by operation of the prior and posterior distributions. Particular prior and approximating posterior distributions are disclosed.

Abstract: Systems, methods, and apparatuses for generating and using machine learning models using genetic data. A set of input features for training the machine learning model can be identified and used to train the model based on training samples, e.g., for which one or more labels are known. As examples, the input features can include aligned variables (e.g., derived from sequences aligned to a population level or individual references) and/or non-aligned variables (e.g., sequence content). The features can be classified into different groups based on the underlying genetic data or intermediate values resulting from a processing of the underlying genetic data. Features can be selected from a feature space for creating a feature vector for training a model. The selection and creation of feature vectors can be performed iteratively to train many models as part of a search for optimal features and an optimal model.

Abstract: A method for predicting air quality with the aid of machine learning models includes: (A) providing air pollution data to perform an eXtreme Gradient Boosting (XGBoost) regression algorithm for obtaining a XGBoost prediction value; (B) providing the air pollution data to perform a Long Short-Term Memory (LSTM) algorithm for obtaining an LSTM prediction value; (C) combining the air pollution data, the XGBoost prediction value and the LSTM prediction value to generate air pollution combination data; (D) performing an XGBoost classification algorithm to obtain a suggestion for whether to issue an air pollution alert; and (E) performing the XGBoost regression algorithm on the air pollution combination data to obtain an air pollution prediction value. Two layers of machine learning models are built, and a situation where prediction results are too conservative when a single model does not have enough data can be improved.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for determining image quality. The method comprises: acquiring a to-be-recognized image and facial region information used for indicating a facial region in the to-be-recognized image; extracting a face image from the to-be-recognized image on the basis of the facial region information; inputting the face image into a pre-trained convolutional neural network to obtain probabilities of each pixel comprised in the face image belonging to a category indicated by each category identifier in a preset category identifier set; inputting the face image into a pre-trained key face point positioning model to obtain coordinates of each key face point comprised in the face image; determining a probability of the face image being obscured on the basis of the probabilities and the coordinates; and determining whether the quality of the face image is up to standard on the basis of the probability.

Abstract: A computerized prediction guided learning method for classification of sequential data performs a prediction learning and a prediction guided learning by a computer program of a computerized machine learning tool. The prediction learning uses an input data sequence to generate an initial classifier. The prediction guided learning may be a semantic learning, an update learning, or an update and semantic learning. The prediction guided semantic learning uses the input data sequence, the initial classifier and semantic label data to generate an output classifier and a semantic classification. The prediction guided update learning uses the input data sequence, the initial classifier and label data to generate an output classifier and a data classification. The prediction guided update and semantic learning uses the input data sequence, the initial classifier and semantic and label data to generate an output classifier, a semantic classification and a data classification.