Abstract: An artificial neural network that includes first subnetworks to implement known functions and second subnetworks to implement unknown functions is trained. The first subnetworks are trained separately and in parallel on corresponding known training datasets to determine first parameter values that define the first subnetworks. The first subnetworks are executing on a plurality of processing elements in a processing system. Input values from a network training data set are provided to the artificial neural network including the trained first subnetworks. Error values are generated by comparing output values produced by the artificial neural network to labeled output values of the network training data set. The second subnetworks are trained by back propagating the error values to modify second parameter values that define the second subnetworks without modifying the first parameter values. The first and second parameter values are stored in a storage component.

Abstract: A system and method are described for efficiently utilizing optimized implementations of computational patterns in an application. In various implementations, a computing system includes at least one or more processors, and these one or more processors and other hardware resources of the computing system process a variety of applications. Sampled, dynamic values of hardware performance counters are sent to a trained data model. The data model provides characterization of the computational patterns being used and the types of workloads being processed. The data model also indicates whether the identified computational patterns already use an optimized version. Later, a selected processor determines a given unoptimized computational pattern is no longer running and replaces this computational pattern with an optimized version. Although the application is still running, the processor performs a static replacement. On a next iteration of the computational pattern, the optimized version is run.

Abstract: A method for reducing power variations resulting from changes in processor workload includes communicating a power dip condition to a workload scheduler of a processor device in response to identifying the power dip condition. One or more target power workloads are assigned for execution at the processor device based at least in part on the power dip condition. Further, each of the one or more target power workloads is associated with a known power load.

Abstract: Values of physical variables that represent a first state of a first physical system are estimated using a deep learning (DL) algorithm that is trained based on values of physical variables that represent states of other physical systems that are determined by one or more physical equations and subject to one or more conservation laws. A physics-based model modifies the estimated values based on the one or more physical equations so that the resulting modified values satisfy the one or more conservation laws.

Abstract: An apparatus includes one or more processors that are configured to determine a pixel-by-pixel bounds for a perturbed image, generate an adversarial example using an adversarial example generation technique, and modify the adversarial example to generate the perturbed image based on the pixel-by-pixel bounds. When an initial perturbed image does not reside within the pixel-by-pixel bounds, the one or more processors adjust the initial perturbed image to generate the perturbed image by a Weber-Fechner based adversarial perturbation to reside within the pixel-by-pixel bounds. The one or more processors provide the perturbed image to a computing device in an image-based Completely Automated Public Turing Test to tell Computers and Humans Apart (CAPTCHA).

Abstract: An artificial neural network that includes first subnetworks to implement known functions and second subnetworks to implement unknown functions is trained. The first subnetworks are trained separately and in parallel on corresponding known training datasets to determine first parameter values that define the first subnetworks. The first subnetworks are executing on a plurality of processing elements in a processing system. Input values from a network training data set are provided to the artificial neural network including the trained first subnetworks. Error values are generated by comparing output values produced by the artificial neural network to labeled output values of the network training data set. The second subnetworks are trained by back propagating the error values to modify second parameter values that define the second subnetworks without modifying the first parameter values. The first and second parameter values are stored in a storage component.