A student model may be trained in two stages by using two teacher models, respectively. The first teacher model has been trained with a pretraining dataset. The second teacher model has been trained with a training dataset that is specific to a task to be performed by the student model. In the first stage, the student model may be generated based on a structure of the first teacher model. Internal parameters of the student model are adjusted through a pretraining process based on the first teacher model and the pretraining dataset. Weights of the student model may be pruned during the pretraining process. In the second stage, a sparsity mask is generated for the student model to lock the sparsity pattern generated from the first stage. Further, some of the internal parameters of the student model are modified based on the second teacher model and the training dataset.

An approach for a non-factoid question answering framework across tasks and domains may be provided. The approach may include training a multi-task joint learning model in a general domain. The approach may also include initializing the multi-task joint learning model in a specific target domain. The approach may include tuning the joint learning model in the target domain. The approach may include determining which task of the multiple tasks is more difficult for the multi-task joint learning model to learn. The approach may also include dynamically adjusting the weights of the multi-task joint learning model, allowing the model to concentrate on learning the more difficult learning task.

An intelligent dog collar for monitoring physiological parameters of a dog, comprising: a movement sensor unit comprising an accelerometer and/or a gyrometer, wherein the movement sensor unit is configured to detect raw movement signals of the dog collar, a storage module storing a trained neural network, the neural network being configured to determine a physiologic information into raw movement signals detected by the movement sensor unit, a processing unit connected to the movement sensor unit and configured to operate the trained neural network, a memory configured to store the identified physiologic information, an interface for transmitting to a communication device the identified physiologic information.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support configuration of cloud-based functionality. A configuration device is provided and includes a data processing module, a modelling module, and a loading module. The data processing module provides functionality for compiling information for use in configuring the cloud-based functionality in a requirements compliant manner. The modelling module may include various machine learning modules configured to evaluate configuration workbooks for compliance with requirements specified by a user. The modelling module may output recommendations for improving compliance of the configuration workbooks and appropriate changes may be made. The loading module may be configured to obtain templates applicable to the cloud-based functionality being configured and to extract appropriate data from the (updated) configuration workbooks. The extracted data may then be loaded into the obtained templates for use in configuring the cloud-based functionality in a regulatory compliant manner.

A platform is positioned within an environment. The platform includes an image capture system connected to a controller implementing a neural network. The neural network is trained to associate visual features within the environment with a target object utilizing a known set of input data examples and labels. The image capture system captures input images from the environment and the neural network recognizes features of one or more of the input images that at least partially match one or more of the visual features within the environment associated with the target object. The input images that contain the visual features within the environment that at least partially match the target object are labeled, a geospatial position of the target object is determined based upon pixels within the labeled input images, and a class activation map is generated, which is then communicated to a supervisory system for action.

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.

Systems and methods for calculating external bone loss for alignment of pre-diseased joints comprising: generating a three-dimensional (“3D”) computer model of an operative area from at least two two-dimensional (“2D”) radiographic images, wherein at least a first radiographic image is captured at a first position, and wherein at least a second radiographic image is captured at a second position, and wherein the first position is different than the second position; identifying an area of bone loss on the 3D computer model; and applying a surface adjustment algorithm to calculate an external missing bone surface fitting the area of bone loss.

Systems and methods for determining whether an electronic document constitutes vouching evidence is provided. The system may receive ERP item data and generate hypothesis data based thereon, and may receive electronic document data and extract ERP information therefrom. The system may then apply one or more models to compare the hypothesis data to the extracted ERP information to determine whether the electronic document constitutes vouching evidence for the ERP item. Systems and methods for verifying an assertion against a source document are provided. The system may receive first data indicating an unverified assertion and second data comprising a plurality of source documents. The system may apply one or more extraction models to extract a set of key data from the plurality of source documents and may apply one or more matching models to compare the first data to the set of key data to determine whether vouching criteria are met.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing brain emulation neural networks using compressed matrix representations. One of the methods includes obtaining a network input; and processing the network input using a neural network to generate a network output, comprising: processing the network input using an input subnetwork of the neural network to generate an embedding of the network input; and processing the embedding of the network input using a brain emulation subnetwork of the neural network, wherein the brain emulation subnetwork has a brain emulation neural network architecture that represents synaptic connectivity between a plurality of biological neurons in a brain of a biological organism, the processing comprising: obtaining a compressed matrix representation of a sparse matrix of brain emulation parameters; and applying the compressed matrix representation to the embedding of the network input to generate a brain emulation subnetwork output.

A method and system of creating a knowledge graph includes capturing information of a user interacting with given data, as user interaction data. The user interaction data is structured as a trail of actions over time. An ontology for a domain related to the user interaction data is received. Each action of the trail of actions is matched onto entities of the ontology. The knowledge graph is created based on the ontology having the matched actions.