A method and system are provided which facilitate construction of an ensemble of neural network kernel classifiers. The system divides a training set into partitions. The system trains, based on the training set, a first neural network encoder to output a first set of features, and trains, based on each respective partition of the training set, a second neural network encoder to output a second set of features. The system generates, for each respective partition, based on the first and second set of features, kernel models which output a third set of features. The system classifies, by a classification model, the training set based on the third set of features. The generated kernel models for each respective partition and the classification model comprise the ensemble of neural network kernel classifiers. The system predicts a result for a testing data object based on the ensemble of neural network kernel classifiers.

Methods and systems for performing diagnostic functions for a deep learning model are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a deep learning model configured for determining information from an image generated for a specimen by an imaging tool. The one or more components also include a diagnostic component configured for determining one or more causal portions of the image that resulted in the information being determined and for performing one or more functions based on the determined one or more causal portions of the image.

Systems and methods of the present invention provide for: receiving a digital image data; modifying the digital image data to reduce a width of a feature within the digital image data; executing a dimension reduction process on the feature; storing a feature vector comprising: at least one feature for each of the received digital image data, and a correct or incorrect label associated with each feature vector; selecting the feature vector from a data store; training a classification software engine to classify each feature vector according to the label; classifying the image data as correct or incorrect according to a classification software engine; and generating an output labeling a second digital image data as correct or incorrect.

Systems and methods for construction zone segmentation are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The target domain includes construction zones scenes having various objects. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.

A method and system are used for managing and classifying electronic document images. Each of the electronic document images is divided into an array of image segments. The method extracts image features from each of the image segments to obtain numerical coefficients for each of the image segments. The numerical coefficients are compared with each other to generate sub-codes. A classification code is determined as a combination of the sub-codes. The classification codes of a plurality of electronic document images can be stored in a database for further analysis. Based on the classification codes, similarity rates between at two document images can be determined.

A method is described which includes receiving a point cloud having a plurality of data points each representing a 3D location in a 3D space, the point cloud being obtained using a detection and ranging (DAR) sensor. For each data point, associating the data point with a 3D volume containing the 3D location of the data point, the 3D volume being defined using a 3D lattice that partitions the 3D space based on spherical coordinates. For at least one 3D volume, the data points are sorted within the 3D volume based on at least one dimension of the 3D lattice; and the sorted data points are stored as a set of ordered data points. The method also includes performing feature extraction on the set of ordered data points to generate a set of ordered feature vectors and providing the set of ordered feature vectors to perform a machine learning inference task.

An example method includes obtaining, a virtual model of a portion of an anatomy of a patient obtained from a virtual surgical plan for an orthopedic joint repair surgical procedure to attach a prosthetic to the anatomy; identifying, based on data obtained by one or more sensors, positions of one or more physical markers positioned relative to the anatomy of the patient; and registering, based on the identified positions, the virtual model of the portion of the anatomy with a corresponding observed portion of the anatomy.

An object identification and collection method is disclosed. The method includes receiving a pick-up path that identifies a route in which to guide an object-collection system over a target geographical area to pick up objects, determining a current location of the object-collection system relative to the pick-up path, and guiding the object-collection system along the pick-up path over the target geographical area based on the current location. The method further includes capturing images in a direction of movement of the object-collection system along the pick-up path, identifying a target object in the images; tracking movement of the target object through the images, determining that the target object is within range of an object picker assembly on the object-collection system based on the tracked movement of the target object, and instructing the object picker assembly to pick up the target object.

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

This disclosure relates to systems, media, and methods for updating motion models using sensor data. In an embodiment, the system may perform operations including receiving sensor data from at least one motion sensor; generating training data based on at least one annotation associated with the sensor data and at least one data manipulation; receiving at least one experiment parameter; performing a first experiment using the training data and the at least one experiment parameter to generate experiment results; and performing at least one of: update or validate a first motion model based on the experiment results.