Abstract: Systems, devices and methods are provided for building learning machines using learning machines. The system generally includes a reference learning machine, a target learning machine being built, a component analyzer module configured to analyze inputs from the reference learning machine, the target learning machine, a set of test signals, and a list of components in the reference learning machine and the target learning machine, and return a set of output values for each component on the list of components. The system further includes a component tuner module configured to modify different components in the target learning machine based on the set of output values and a component mapping, thereby resulting in a tuned learning machine.

Abstract: Systems, devices and methods are provided for building learning machines using learning machines. The system generally includes a reference learning machine, a target learning machine being built, a component analyzer module configured to analyze inputs from the reference learning machine, the target learning machine, a set of test signals, and a list of components in the reference learning machine and the target learning machine, and return a set of output values for each component on the list of components. The system further includes a component tuner module configured to modify different components in the target learning machine based on the set of output values and a component mapping, thereby resulting in a tuned learning machine.

Abstract: Systems and methods for selecting unlabeled data for building and improving the performance of a learning machine are disclosed. In an aspect, such a system may include a reference learning machine, a set of labeled data, and a learning machine analyzer. The learning machine analyzer is configured to receive the reference learning machine and the set of labeled data as inputs and analyze the inner working of the reference learning machine to produce a selected set of unlabeled data. In an aspect, the learning machine analyzer identifies and measures a relation between different input data samples and finds all pairwise relations to construct a relational graph. In an aspect, the relational graph visualizes how much the different input data samples are like each other in higher dimensions inside the reference learning machine.

Abstract: Disclosed are example embodiments of systems and methods for selecting components for building graph-based learning machines. An example system for selecting components for building graph-based learning machines includes a reference learning machine, one or more test signals, and a component analyzer module. The component analyzer module is configured to analyze, using the one or more test signals, one or more component in the reference machine by ranking different components in the reference learning machine in terms of their efficiency and effectiveness.

Abstract: A method and apparatus for predicting hospital bed exit events from video camera systems is disclosed. The system processes video data with a deep convolutional neural network consisting of five main layers: a 1×1 3D convolutional layer used for generating feature maps from raw video data, a context-aware pooling layer used for rectifying data from different camera angles, two fully connected layers used for applying pre-trained deep features, and an output layer used to provide a likelihood of a bed exit event.

Abstract: A method for monitoring a patient in a bed may involve capturing images of the patient with multiple cameras in a vicinity of the bed, wirelessly transmitting the images of the patient from the multiple cameras to a processor including a memory device, processing the images to provide processed image data pertaining to a position of the patient relative to the bed to a user, and analyzing the processed image data to determine whether the patient is exiting the bed. The method may also optionally involve providing an alarm indicating that the patient is exiting the bed.

Abstract: Systems, devices and methods are provided for building learning machines using learning machines. The system generally includes a reference learning machine, a target learning machine being built, a component analyzer module configured to analyze inputs from the reference learning machine, the target learning machine, a set of test signals, and a list of components in the reference learning machine and the target learning machine, and return a set of output values for each component on the list of components. The system further includes a component tuner module configured to modify different components in the target learning machine based on the set of output values and a component mapping, thereby resulting in a tuned learning machine.

Abstract: A method and apparatus for predicting hospital bed exit events from video camera systems is disclosed. The system processes video data with a deep convolutional neural network consisting of five main layers: a 1×1 3D convolutional layer used for generating feature maps from raw video data, a context-aware pooling layer used for rectifying data from different camera angles, two fully connected layers used for applying pre-trained deep features, and an output layer used to provide a likelihood of a bed exit event.

Abstract: A method for monitoring a patient in a bed may involve capturing images of the patient with multiple cameras in a vicinity of the bed, wirelessly transmitting the images of the patient from the multiple cameras to a processor including a memory device, processing the images to provide processed image data pertaining to a position of the patient relative to the bed to a user, and analyzing the processed image data to determine whether the patient is exiting the bed. The method may also optionally involve providing an alarm indicating that the patient is exiting the bed.

Abstract: There is disclosed a novel system and method for building artificial neural networks for a given task. In an embodiment, the method utilizes one or more network models that define the probabilities of nodes and/or interconnects, and/or the probabilities of groups of nodes and/or interconnects, from sets of possible nodes and interconnects existing in a given artificial neural network. These network models can be constructed based on the properties of one or more artificial neural networks, or constructed based on desired architecture properties. These network models are then used to build combined network models using a model combiner module. The combined network models and random numbers generated by a random number generator module are then used to build one or more new artificial neural network architectures. New artificial neural networks are then built based on the newly built artificial neural network architectures and are trained for a given task.