Abstract: A system, electronic device and method for improved neural network training are provided. The electronic device includes: a processor, a memory storing a Generative adversarial network (GAN) to learn from unlabeled data by engaging a generative model in an adversarial game with a discriminator; and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for training the Generative adversarial network using a regularizer to encourage the discriminator to properly use its capacity and hidden representations of the discriminator to have high entropy.

Abstract: A system and method for determining a reliability score indicative of a level of fidelity between high dimensional (HD) data and corresponding dimension-reduced (LD) data are provided. The system comprises a processor, and a non-transitory computer-readable medium having stored thereon program instructions executable by the processor. The processor is configured to perform the method.

Abstract: Systems and methods for computationally generating a set of more “stable” configuration default values that are used for traceability and improving reproducibility of machine learning approaches. Hash values are generated based on a merged/modified configuration and both configuration content and hash are stored together in one or more data structures. These data structures can be used to link back to the actual values used in experiments.

Abstract: Robotic systems, methods of operation of robotic systems, and storage media including processor-executable instructions are disclosed herein. The system may include a robot, at least one processor in communication with the robot, and an operator interface in communication with the robot and the at least one processor. The method may include executing a first set of autonomous robot control instructions which causes a robot to autonomously perform the at least one task in an autonomous mode, and generating a second set of autonomous robot control instructions from the first set of autonomous robot control instructions and a first set of environmental sensor data received from a senor. The second set of autonomous robot control instructions when executed causes the robot to autonomously perform the at least one task. The method may include producing at least one signal that represents the second set of autonomous robot control instructions.