An anomaly detection apparatus includes an anomaly detection unit configured to perform anomaly detection on time series data. The anomaly detection unit includes an encoding unit configured to encode the time series data by using a plurality of LSTM cells, an attention layer configured to calculate a weight of attention on an output from the encoding unit, a context generation unit configured to generate a context vector by applying the weight to the output from the encoding unit, and a decoding unit configured to reconfigure the time series data by using the plurality of LSTM cells in accordance with the context vector, and thereby, enables improvement in accuracy for the anomaly detection and efficient learning.

The present disclosure relates to a communication system based on a neural network model, and a configuration method therefor. The communication system includes at least one master node and multiple child nodes that are in communication connection with the master node, and a child node neural network model is configured in each of the multiple child nodes. The configuration method for the communication system includes: obtaining feature information of the multiple child nodes; and dynamically configuring the child node neural network models on the basis of the obtained feature information.

The invention provides a method for realizing a multi-channel convolutional recurrent neural network EEG emotion recognition model using transfer learning, the method uses a dual-channel one-dimensional convolutional neural network model constructed based on three heartbeats recognition method as the source domain model for transferring, to obtain a multi-channel convolutional recurrent neural network EEG emotion recognition model with EEG signal as the target domain, it solves the problem of scarcity of EEG labeling data, and can improve the accuracy of EEG emotion prediction. The accuracy of data processing is improved by decomposing and normalizing the EEG data set; the transferred multi-channel convolutional neural network extracts the features of multi-channel EEG signals in EEG data set; combined with the recurrent neural network, sequence modeling is carried out to extract multi-channel fused emotional information; the feature redistribution is realized by adaptive attention model and weighted feature fusion, and the complete feature tensor is obtained.

A computer divides a character string included in text data into a plurality of tokens. The computer searches, by performing matching processing between a token string indicating a specific number of consecutive tokens among the plurality of tokens and dictionary information including a plurality of named entities, the plurality of named entities for a similar named entity whose similarity to the token string is equal to or more than a threshold. The computer converts matching information indicating a result of the matching processing between the token string and the similar named entity into first vector data. The computer generates input data by using a plurality of pieces of vector data converted from the plurality of tokens and the first vector data. The computer generates a named entity recognition model that detects a named entity by performing machine learning using the input data.

Implementations disclosed describe methods and systems to perform the methods of optimizing a size of memory used for accumulation of neural node outputs and for supporting multiple computational paths in neural networks. In one example, a size of memory used to perform neural layer computations is reduced by performing nodal computations in multiple batches, followed by rescaling and accumulation of nodal outputs. In another example, execution of parallel branches of neural node computations include evaluating, prior to the actual execution, the amount of memory resources needed to execute a particular order of branches sequentially and select the order that minimizes this amount or keeps this amount below a target threshold.

Aspects described provide systems and methods that relate generally to image analysis and, more specifically, identifying individual components and elements in an image. The systems and methods include a valuation application executing one or more application program interfaces (APIs) communicating with one or more websites via a network, where the user is prompted to enter information and/or take pictures or videos of their vehicle that they would like to sell. The valuation application utilizes a machine learning model to identify and value the various vehicle components within the images and videos. Based on the machine learning model, the valuation application identifies each component according to the images and videos and performs a search to determine the value of the components identified. The valuation application tabulates and summarizes the vehicle component resale values and resell information for the user to view.

Examples described herein provide a method that includes performing a first scan of an object to generate first scan data. The method further includes detecting a defect on a surface of the object by analyzing the first scan data to identify a region of interest containing the defect by comparing the first scan data to reference scan data. The method further includes performing a second scan of the region of interest containing the defect to generate second scan data, the second scan data being higher resolution scan data than the first scan data. The method further includes combining the first scan data and the second scan data to generate a point cloud of the object.

There is a need to accurately and dynamically predicting a probability for an event and a likely cause for the event prior to the event occurring using collected data from disparate data sources. This need can be addressed, for example, by generating an event prediction data object by utilizing an event prediction machine learning model, wherein the event prediction data object describes an event likelihood prediction and in an instance where the event likelihood prediction is an affirmative likelihood prediction, one or more fall cause predictions; and performing one or more prediction-based actions based at least in part on the event likelihood prediction.

A computer system (which may include one or more computers) that trains a neural network is described. During operation, the computer system may train the neural network based at least in part on a set of hyperparameters, where the training includes computing weights associated with neurons in the neural network. Moreover, during the training, the computer system may dynamically adapt one or more first hyperparameters in the set of hyperparameters based at least in part on a measure corresponding to a local geometry of a loss landscape at or proximate to a current location in the loss landscape. Note that the dynamic adapting based at least in part on the measure is separate from or in addition to a predefined adaptation of one or more second hyperparameters the set of hyperparameters based on a predefined number of iterations or cycles in the training or a predefined scaling factor.

A method comprises collecting data corresponding to operation of a user interface, analyzing the data and generating a dependency tree based at least in part on the analysis. The analyzing and the generating are performed using one or more machine learning techniques. The dependency tree comprises a plurality of nodes respectively corresponding to a plurality of components of the user interface and is organized at least in part according to one or more dependent relationships between the plurality of components. Based at least in part on a structure of the dependency tree, one or more test cases for the user interface are executed.