A method for stabilizing an image based on artificial intelligence includes acquiring tremor detection data with respect to the image, the tremor detection data acquired from two or more sensors; outputting stabilization data for compensating for an image shaking, the stabilization data outputted using an artificial neural network (ANN) model trained to output the stabilization data based on the tremor detection data; and compensating for the image shaking using the stabilization data. A camera module includes a lens; an image sensor to output an image captured through the lens; two or more sensors to output tremor detection data with respect to the image; a controller to output stabilization data based on the tremor detection data using an ANN model; and a stabilization unit to compensate for an image shaking using the stabilization data. The ANN model is trained to output the stabilization data based on the tremor detection data.

A sensor transformation attention network (STAN) model including sensors configured to collect input signals, attention modules configured to calculate attention scores of feature vectors corresponding to the input signals, a merge module configured to calculate attention values of the attention scores, and generate a merged transformation vector based on the attention values and the feature vectors, and a task-specific module configured to classify the merged transformation vector is provided.

A method of using a computing device to self-train a machine learning model with an incomplete dataset including original observational data. The method includes receiving a labeled training data, the labeled training data for training a machine learning model. Counterfactual unlabeled training data is received. One or more labels are predicted for the counterfactual unlabeled training data. The machine learning model is trained based upon the labeled training data, the counterfactual unlabeled training data, and the predicted one or more labels for the unlabeled training data. The machine learning model reduces bias in original observational data. An evaluation of the predicted one or more labels is received based on corresponding artificial intelligence explanations provided by an artificial intelligence explainability model.

Embodiments including a method and apparatus for correction of a global navigation satellite system (GNSS) are described. In one example, the apparatus includes a communication interface and a processor. The communication interface is configured to a plurality of GNSS signals. The GNSS signals may include at least one almanac value and at least one ephemeris value. The processor is configured to generate a spatio-temporal graph model based on the at least one almanac value, the at least one ephemeris value, and a predetermined offset value for a base location. The spatio-temporal graph model analyzes subsequent GNSS signals to determined a predicted offset or a corrected GNSS position.

Disclosed are a method and system for changing a structure of a deep learning model based on a change in resolution of input data. The method of changing a structure of a deep learning model may include generating, by the at least one processor, a plurality of input data having different resolution by performing various resolution changes on input data having given resolution, performing, by the at least one processor, inference on each of the plurality of generated input data through a deep learning model, checking, by the at least one processor, the size of a feature map output by each of layers included in the deep learning model while the inference is performed, and changing, by the at least one processor, the structure of at least one of the layers based on the checked size of the feature map.

A method, integrated circuit, and a computer readable medium that stores instructions for reducing IO traffic from a global or remote memory unit to a buffer of a neural network unit, by using overlap rows of an input feature map tile.

Various embodiments are generally directed to techniques for prediction based machine learning (ML) models, such as to utilize a ML model to generate predictions based on the output of another ML model. Some embodiments are particularly directed to a secondary ML model that revises predictions generated by a primary ML model based on structured input data. In many embodiments, the secondary ML model may utilize predictions from the primary ML model to learn metadata regarding the structured input data. In many such embodiments, the metadata regarding the structured input data may be used to revise the predictions from the primary ML model. For example, the secondary ML model may utilize a structure of the input data combined with patterns in the predictions from the primary ML model to revise the predictions from the primary ML model.

A method can be used to predict risk using machine learning models having efficient feature learning. A risk prediction model can be applied to time-series data associated with a target entity to generate a risk indicator. The risk prediction model can include a feature learning model for generating features from the time-series data. The risk prediction model can also include a risk classification model for generating the risk indicator. The feature learning model can include filters and can be trained. Parameters of the risk prediction model can be adjusted to minimize a loss function associated with risk indicators. An updated risk prediction model can be generated by removing a filter from an original set of filters based on influencing scores of the original filters. The risk indicator can be transmitted to a computing device for use in controlling access of the target entity to a computing environment.

A character recognition method includes inputting an input image of a document, with the input image including a plurality of characters; selecting the plurality of characters through an object detection module to form at least one character region; separating the plurality of characters in the at least one character region to form a plurality of character boxes; performing calculation to determine a format of a character in each of the plurality of character boxes; recognizing the characters in the at least one character region through an object recognition module to determine a symbol content of the character in each of the plurality of character boxes; and converting the plurality of characters according to the format and symbol content of the character in each of the plurality of character boxes, and outputting corresponding editable characters.

Method and system relating generally to convolution is disclosed. In such a method, an image patch is selected from input data for a first channel of a plurality of input channels of an input layer. The selected image patch is transformed to obtain a transformed image patch. The transformed image patch is stored. Stored is a plurality of predetermined transformed filter kernels. A stored transformed filter kernel of the plurality of stored predetermined transformed filter kernels is element-wise multiplied by multipliers with the stored transformed image patch for a second channel of the plurality of input channels different from the first channel to obtain a product. The product is inverse transformed to obtain a filtered patch for the image patch.