A method for optimizing a data model is used in a device. The device acquires data information and selecting at least two data models according to the data information, and utilizes the data information to train the at least two data models. The device acquires each accuracy of the at least two data models, determines a target data model which has greatest accuracy between the at least two data models, and optimizes the target data model.

A non-volatile memory device includes an array of non-volatile memory cells that are configured to store weights of a neural network. Associated with the array is a data latch structure that includes a page buffer, which can store weights for a layer of the neural network that is read out of the array, and a transfer buffer, that can store inputs for the neural network. The memory device can perform multiply and accumulate operations between inputs and weight of the neural network within the latch structure, avoiding the need to transfer data out of the array and associated latch structure for portions of an inference operation. By using binary weights and inputs, multiplication can be performed by bit-wise XNOR operations. The results can then be summed and activation applied, all within the latch structure.

According to an embodiment, the recognition apparatus includes an image interface, an input interface, and a processor. The image interface is configured to acquire a display screen image from an input device for inputting a character string included in a captured image in which recognition of the character string according to a first algorithm fails. The input interface is configured to input the character string to the input device. The processor is configured to acquire a result of character recognition processing performed on the display screen image according to a second algorithm different from the first algorithm, and input the character string based on the result of the character recognition processing to the input device through the input interface.

Techniques are disclosed relating to the execution of machine learning models on client devices, particularly in the context of transaction risk evaluation. This reduces computational burden on server systems. In various embodiments, a server system may receive, from a client device, a request to perform a first operation and select a first machine learning model, from a set of machine learning models, to send to the client device. In some embodiments the first machine learning model is executable, by the client device, to generate model output data for the first operation based on one or more encrypted input data values that are encrypted with a cryptographic key inaccessible to the client device. The server system may send the first machine learning model to the client device and then receive, from the client device, a response message that indicates whether the first operation is authorized based on the model output data.

Techniques for providing improved optical character recognition (OCR) for receipts are discussed herein. Some embodiments may provide for a system including one or more servers configured to perform receipt image cleanup, logo identification, and text extraction. The image cleanup may include transforming image data of the receipt by using image parameters values that optimize the logo identification, and performing logo identification using a comparison of the image data with training logos associated with merchants. When a merchant is identified, a second image clean up may be performed by using image parameter values optimized for text extraction. A receipt structure may be used to categorize the extracted text. Improved OCR accuracy is also achieved by applying on format rules of the receipt structure to the extracted text.

This application provides a method for adjusting a resource of an intelligent analysis device and an apparatus. The method includes: obtaining status information of an intelligent analysis device that accesses a surveillance platform and application information deployed on the intelligent analysis device, where the status information includes resource usage and a quantity of bound cameras; after a camera accesses the surveillance platform, selecting a to-be-bound intelligent analysis device for the camera based on the status information and the application information of the intelligent analysis device that accesses the surveillance platform; and sending, to the selected intelligent analysis device, a command for binding the camera. In this way, the resource of the intelligent analysis device may be automatically allocated. This improves processing efficiency and avoids low efficiency caused by manual processing.

Systems and methods for features engineering, in which internal and external signals are received and fused. The fusing is based on meta-data of each of the one or more internal signals and each of the one or more external signals. A set of features is generated based on one or more valid combinations that match a transformation input, the transformation forming part of library of transformations. Finally, a set of one or more features is selected from the plurality of features, based on a predictive strength of each feature. The set of selected features can be used to train and select a machine learning model.

An information processing apparatus includes a processor configured to acquire a first recognition result and a first recognition probability on target data from a first recognizer, acquire a second recognition result and a second recognition probability on the target data from a second recognizer, execute checking of the first recognition result and the second recognition result, and execute first control in a case where the first recognition result and the second recognition result match each other as a result of the checking. The first control is control for executing either of first processing or second processing on the matched recognition result and outputting a processing result based on at least one of the first recognition probability or the second recognition probability. A human workload for the first processing is smaller than a human workload for the second processing.

An analysis apparatus according to one or more embodiments may identify the classes of features included in object data using a plurality of discriminators that are respectively configured to discriminate the presence of features of classes different to each other; and determines that a first data portion, with respect to which discrimination is established by one of the plurality of discriminators, but discrimination is not established by the remaining discriminators, includes a feature of the particular class that is discriminated by the one discriminator, and determines that a second data portion, with respect to which discrimination is established by all of the discriminators including the one discriminator, does not include a feature of that particular class.

The present invention provides techniques for learning Mahalanobis distance similarity metrics from data for individually fair machine learning models. In one aspect, a method for learning a fair Mahalanobis distance similarity metric includes: obtaining data with similarity annotations; selecting, based on the data obtained, a model for learning a Mahalanobis covariance matrix Σ; and learning the Mahalanobis covariance matrix Σ from the data using the model selected, wherein the Mahalanobis covariance matrix Σ fully defines the fair Mahalanobis distance similarity metric.