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.

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.

A non-transitory computer-readable recording medium storing a program that causes a computer to execute a process, the process includes generating, based on an index value related to an evaluation function value, a first candidate target from combinatorial targets in a combinatorial optimization problem that minimizes the evaluation function value under a plurality of constraint conditions, analyzing, based on a first result obtained by solving and optimizing based on the first candidate target, a combination that is included in the first result and that is a constraint violation, selecting, from among the combinatorial targets, a target related to resolving of the constraint violation that has been analyzed, obtaining, based on a second candidate target that include the selected combinatorial target and the first result, a second result which is optimized, and determining a solving result of the combinatorial optimization problem based on an evaluation result of the second result.

Various embodiments of a computer-implemented system which learns textual representations while filtering out potentially personally identifying data and retaining semantic meaning within the textual representations are disclosed herein.

Disclosed examples access media impression data via one or more wireless communications, the media impression data including panel data obtained from a meter and impression information obtained after an access of media at a computing device; determine an audience deduplication based on the panel data; determine odds ratios for platform combinations based on the audience deduplication; determine posterior distributions for the media based on the odds ratios; perform a sequential odds ratio insertion technique based on the posterior distributions to determine unique audience sizes; align the unique audience sizes based on a constraint; and generate a report including the aligned unique audience sizes.

Systems, methods, apparatuses and devices for detecting facial expressions according to EMG signals for a virtual and/or augmented reality (VR/AR) environment, in combination with a system for simultaneous location and mapping (SLAM), are presented herein.

Various embodiments of a computer-implemented system which learns textual representations while filtering out potentially personally identifying data and retaining semantic meaning within the textual representations are disclosed herein.

Systems, methods, apparatuses and devices for detecting facial expressions according to EMG signals for a virtual and/or augmented reality (VR/AR) environment, in combination with a system for simultaneous location and mapping (SLAM), are presented herein.

Systems, methods, apparatuses and devices for detecting facial expressions according to EMG signals for a virtual and/or augmented reality (VR/AR) environment, in combination with a system for simultaneous location and mapping (SLAM), are presented herein.

The present disclosure relates generally to machine learning. More particularly, the present disclosure relates to systems and methods that regularize neural networks by decorrelating neurons or other parameters of the neural networks during training of the neural networks promoting these parameter to innovate over one another.