The present application discloses a method, system, and computer system for building a model associated with a dataset. The method includes receiving a data set, the dataset comprising a plurality of keys and a plurality of key-value relationships, determining a plurality of models to build based at least in part on the dataset, wherein determining the plurality of models to build comprises using the dataset format information to identify the plurality of models, building the plurality of models, and optimizing at least one of the plurality of models.

Systems and methods are described herein for processing electronic medical images to optimize a review order of pathology cases. For example, a plurality of variables and one or more constraints may be received along with a plurality of pathology cases. Each case of the plurality of pathology cases may include one or more medical images of at least one pathology specimen associated with a patient. The medical images from each case, the plurality of variables, and the one or more constraints may be provided as input to a trained system. A sequential order for user review of the plurality of cases to optimize one or more of the plurality of variables based on the one or more constraints may be received as output of the trained system. Each case of the plurality of cases may be automatically provided to a user for review according to the sequential order.

Embodiments described herein provide safe policy improvement (SPI) in a batch reinforcement learning framework for a task-oriented dialogue. Specifically, a batch reinforcement learning framework for dialogue policy learning is provided, which improves the performance of the dialogue and learns to shape a reward that reasons the invention behind human response rather than just imitating the human demonstration.

A method with quantization for a deep learning model includes: determining a second model by quantizing a first model based on a quantization parameter; determining a real value of multi optimization target parameter by testing the second model; calculating a loss function based on the real value of the multi optimization target parameter, an expected value of the multi optimization target parameter, and a constraint value of the multi optimization target parameter; updating the quantization parameter based on the loss function and using the second model as the first model; iteratively executing the foregoing operations until a preset condition is satisfied; and in response to the preset condition being satisfied, determining an optimal quantization parameter and using, as a final quantization model, the first model that executes quantization based on the optimal quantization parameter.

A method for plausibilizing the output of an image classifier which assigns an input image to one or more class(es) of a predefined classification. The method includes: an assignment to one or more class(es) is ascertained for the input image using the image classifier; a relevance assessment function is used to ascertain a spatially resolved relevance assessment of the input image, which indicates which components of the input image have contributed to what degree to the assignment; a generator is trained to generate modifications of the input image that are as satisfactory as possible according to a predefined cost function in view of the optimization goals; based on the result of the training, and/or based on the modifications supplied by the trained generator, a quality measure for the spatially resolved relevance assessment, and/or a quality measure for the relevance assessment function is/are ascertained.

A machine learning system that incorporates arbitrary constraints into deep learning model is provided. The machine learning system provides a set of penalty data points en a set of arbitrary constraints in addition to a set of original training data points. The machine learning system assigns a penalty to each penalty data point in the set of penalty data points. The machine learning system optimizes a machine learning model by solving an objective function based on an original loss function and a penalty loss function. The original loss function is evaluated over a set of original training data points and the penalty loss function is evaluated over the set of penalty data points. The machine learning system provides the optimized machine learning model based on a solution of the objective function.

An optimum input parameter may be determined rapidly. According to an input data dimension number that is a dimension number of input data, a reduced dimension number that is lower than the input data dimension number, and a parallel number, as many searching ranges as the parallel number are determined by determining as many transformation matrices as the parallel number, each transformation matrix being for transforming a space defined by the input data dimension number to a space defined by the reduced dimension number. Inputting input data to a simulator and acquiring an objective function value that is difference between output data and a previously provided observation are repeated a predetermined number of times and a next input parameter is determined using an acquisition function. Inputting to the simulator the determined next input parameter and input data obtained from transformation matrix and determining an objective function value are repeated in parallel, a predetermined number of times, to determine the optimum input parameter.

A system and a method of assessing data corresponding to performance of a player playing a sport/game and providing recommendations for improving the performance are disclosed. The system presents a set of questionnaires corresponding to a first set of elements and a second set of elements to a user. The first set of elements correspond to personality and the second set of elements correspond to an ecosystem of a player or user playing/interested in a sport/game/play. The user provides responses using a user device. The system receives the responses and maps each of the responses with predetermined parameters having weightages. The system determines a score for each of the first set of elements and the second set of elements based on the mapping and assesses the skill, personality and ecosystem of the player. Further, the system presents an aggregate score based on the score for each of the first and second set of elements. The system provides recommendations to the user to improve the game based on the assessment.

A method for determining a sensor configuration in a vehicle which includes a plurality of sensors. The method comprises: (i) establishing a preliminary sensor configuration for the vehicle, which sensor configuration includes a first number of real sensors, each of which outputting a real sensor signal, (ii) determining whether at least one of the real sensors can be replaced by a virtual sensor, and (iii) changing the preliminary sensor configuration into a final sensor configuration which includes a second number of real sensors and at least one virtual sensor which has been determined to replace at least one of the real sensors, wherein the second number is smaller than the first number.

A generation apparatus is configured to access a set of pieces of learning data each being a combination of a value of an explanatory variable and a value of an objective variable, a function family list including, of functions each indicating a physical law and an attribute of each of the functions, at least the functions, and search range limiting information for limiting a search range of the function family list, wherein the processor is configured to execute: first generation processing of generating a first prediction expression by setting a first parameter for the explanatory variable to a first function included in the function family list; first calculation processing of calculating, based on the search range limiting information, a first conviction degree relating to the first prediction expression; and first output processing of outputting the first prediction expression and the first conviction degree.