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.

The invention relates to a method for person re-identification based on deep model with multi-loss fusion training strategy. The method uses a deep learning technology to perform preprocessing operations such as flipping, clipping, random erasing and style transfer, and then feature extraction is performed through a backbone network model; joint training of a network is performed by fusing a plurality of loss functions. Compared with other deep learning-based person re-identification algorithms, the present invention greatly improves the performance of person re-identification by adopting a plurality of preprocessing modes, the fusion of three loss functions and effective training strategy.

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 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.

A computer-implemented method, computer program product, and computer system for automated model predictive control. The computer system trains multiple step look-ahead regression models, using historical states and historical actions for a to-be-optimized system, for each timestep of a past time horizon. Regression models may be either linear or nonlinear in order to capture process dynamics and nonlinearity. The computer system generates optimization constraints for each timestep of a future time horizon. The computer system generates optimization variables, based on the multiple step look-ahead regression models, for each timestep of the future time horizon. The computer system constructs a mixed integer linear programming based optimization model that includes an objective function, the optimization constraints, and the optimization variables. Nonlinear regression models are converted into piecewise linear approximation functions. The computer system solves the optimization model to produce actions for the to-be-optimized system, over the future time horizon, and recommend commitment-look-ahead actions.

Certain examples relate to a method of color prediction. Data indicative of color characteristics measured from a number of color test patches is obtained. Data indicative of combinations of color resources used to render the color test patches on a color rendering device is obtained. A first predictive model is trained using the data indicative of the combinations of color resources as an input and corresponding data indicative of color characteristics as ground truth outputs. A second predictive model is trained using the output data from the first predictive model as an input and the corresponding data indicative of color characteristics as ground truth outputs. A progressive mapping is implemented by the first and second predictive models to predict color characteristics rendered by the color rendering device given a combination of color resources.

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.