A method is for providing training data for training a data-based system model for operating a technical system by defining a data point determined from input variables for determining at least one output variable depending on which the technical system is operating. The method includes providing training data that are determined with a scenario other than a real operation of the technical system, the training data are defined for data points determined from the input variables, capturing operational data points determined from the input variables in real-world operation of the technical system, and splitting the training data into training data points and validation data points. The method further includes determining a k-Nearest Neighbor tree from the training data points, and determining a first distribution of distance values of distances between each of the validation data points and a predetermined number of next training data points of the training data points.

Various embodiments are generally directed to techniques for determining artificial neural network topologies, such as by utilizing probabilistic graphical models, for instance. Some embodiments are particularly related to determining neural network topologies by bootstrapping a graph, such as a probabilistic graphical model, into a multi-graphical model, or graphical model tree. Various embodiments may include logic to determine a collection of sample sets from a dataset. In various such embodiments, each sample set may be drawn randomly for the dataset with replacement between drawings. In some embodiments, logic may partition a graph into multiple subgraph sets based on each of the sample sets. In several embodiments, the multiple subgraph sets may be scored, such as with Bayesian statistics, and selected amongst as part of determining a topology for a neural network.

Improved (e.g., high-throughput, low-noise, and/or low-artifact) X-ray Microscopy images are achieved using a deep neural network trained via an accessible workflow. The workflow involves selection of a desired improvement factor (x), which is used to automatically partition supplied data into two or more subsets for neural network training. The neural network is trained by generating reconstructed volumes for each of the subsets. The neural network can be trained to take projection images or reconstructed volumes as input and output improved projection images or improved reconstructed volumes as output, respectively. Once trained, the neural network can be applied to the training data and/or subsequent data—optionally collected at a higher throughput—to ultimately achieve improved de-noising and/or other artifact reduction in the reconstructed volume.

Systems and methods are provided for determining faces and bodies of people in an image by adaptively scaling images and by iteratively using a deep neural network for inferencing. A camera captures an image including faces and bodies of people. A face/body determiner determines faces and bodies of people appearing in the image by resizing the image into a predetermined pixel dimension as input to the deep neural network. A region cropper determines a crop region associated with a low level of confidence in detecting faces and bodies that are too small to determine with an acceptable level of confidence. The region cropper resizes the crop region into the predetermined pixel dimension as input to the deep neural network. The face and body determiner determines other faces and bodies appearing in the resized crop region. An aggregator aggregates locations of the determined faces and bodies in the image.

A computing system may include a hyperspace generation engine and a hyperspace processing engine. The hyperspace generation engine may be configured to access a feature vector set, and feature vectors in the feature vector set may represent values for multiple parameters of data points in a dataset. The hyperspace generation engine may further be configured to perform a principal component analysis on the feature vector set and quantize the principal component space into a hyperspace comprised of hyperboxes. The hyperspace processing engine may be configured to process the dataset according to a mapping of the feature vector set into the hyperboxes of the hyperspace.

Example methods, apparatus, and articles of manufacture to classify labels based on images using artificial intelligence are disclosed. An example apparatus includes a regional proposal network to determine a first bounding box for a first region of interest in a first input image of a product; and determine a second bounding box for a second region of interest in a second input image of the product; a neural network to: generate a first classification for a first label in the first input image using the first bounding box; and generate a second classification for a second label in the second input image using the second bounding box; a comparator to determine that the first input image and the second input image correspond to a same product; and a report generator to link the first classification and the second classification to the product.

A method, system and/or computer usable program product for automatically managing the conveying of messages among multiple communication channels including (i) receiving, from a first computing system, an on-line message addressed to a user, (ii) automatically categorizing the message among a predetermined set of message categories stored in memory, (iii) identifying a set of on-line message channels preselected by the addressee user for receiving messages for each of the predetermined set of message categories, (iv) identifying a set of performance metrics stored in memory for optimizing message channel selection, (v) utilizing the performance metrics to automatically select an optimum message channel from the preselected message channels for sending the categorized message to a second computing system of the addressee user, (vi) automatically formatting the categorized message for the optimum message channel, and (vii) sending the formatted message on-line to the second computing system of the addressee user across the optimum message channel.

A computing device accesses video data displaying one or more traffic entities and generates a plurality of sequences from the video data. For each sequence, the computing device identifies a plurality of stimuli in the sequence and applies a machine learning model to generate an output describing the traffic entity. The computing device generates a data structure for storing, for each sequence, information describing the sequence and linking frame indexes of stimuli from the sequence to outputs of the machine learning model. The computing device stores the data structure in association with the video data. Responsive to receiving a selection of a sequence, the computing device loads video data for the sequence. Responsive to receiving a selection of a traffic entity within the video data, the computing device generates a graphical display element including the machine learning model output for the selected traffic entity.

An apparatus for learning an image of a vehicle camera and a method thereof are provided to apply a result of deep learning to all vehicles regardless of the color of a vehicle and the mounting angle (e.g., yaw, roll and pitch) of a camera. The apparatus includes an image input device that inputs an image photographed by a camera mounted on a vehicle, and a controller that masks a fixed area in the image input from the image input device with a pattern image, converts the masked image into a plurality of images having different views, and performs deep learning by using the masked image and the converted plurality of images.

A method includes obtaining, by a local data system manager of a local data system of the local data systems, a portion of unlabeled data from a local data source, performing, using a domain classifier in the local data system manager, a domain classification analysis on the portion of the unlabeled data to identify a domain of the unlabeled data, making a first determination, based on the domain classification, that the domain classification has significantly varied from a previous domain, based on the first determination: performing an adaptive procedure on a local data system image segmentation model to obtain an adapted image segmentation model, and performing a domain reclassification on the domain classifier to obtain an updated domain classifier, and implementing the adapted image segmentation model on the local data system.