A method includes determining a current state of an environment of an autonomous agent, such as a vehicle. The method also includes determining, via a first neural network, a set of actions based on the current state. The method further includes determining whether further analysis of the set of actions is desired. The method selects an action from the set of actions using a model-based solution based on a reward and a risk of the action when further analysis is desired. The method also includes selecting the action from the set of actions according to a metric when further analysis is not desired. The method controls the autonomous agent to perform the selected action.

Datasets for autonomous driving systems and multi-modal scenes may be automatically labeled using previously trained models as priors to mitigate the limitations of conventional manual data labeling. Properly versioned models, including model weights and knowledge of the dataset on which the model was previously trained, may be used to run an inference operation on unlabeled data, thus automatically labeling the dataset. The newly labeled dataset may then be used to train new models, including sparse data sets, in a semi-supervised or weakly-supervised fashion.

Acquiring labeled data can be a significant bottleneck in the development of machine learning models that are accurate and efficient enough to enable safety-critical applications, such as automated driving. The process of labeling of driving logs can be automated. Unlabeled real-world driving logs, which include data captured by one or more vehicle sensors, can be automatically labeled to generate one or more labeled real-world driving logs. The automatic labeling can include analysis-by-synthesis on the unlabeled real-world driving logs to generate simulated driving logs, which can include reconstructed driving scenes or portions thereof. The automatic labeling can further include simulation-to-real automatic labeling on the simulated driving logs and the unlabeled real-world driving logs to generate one or more labeled real-world driving logs. The automatically labeled real-world driving logs can be stored in one or more data stores for subsequent training, validation, evaluation, and/or model management.

A system and method for processing loans includes loan approval decision module that receives input from a loan applicant and collects external data including credit bureau data, bank transaction data, and social media data. The system also includes a machine learning module having a pre-processing subsystem, an automated feature engineering subsystem and a feature statistical assessment subsystem. A business objective determination module and an adverse notice notification module is also provided. The business objective determination module includes a weight optimization company valuation maximization model. A set of models is developed using the machine learning module to predict performance of the borrower based on the business objective determination.

A machine learning based system can identify an entity as the likely subject of a multimodal message (e.g., a social media post having a short text phrase overlaid on an image) by creating embeddings for an image of the multimodal message and one or more string embeddings from text of the multimodal message. The embeddings can be weighted to maximize information gain, then recombined and compared against a result embedding database to identify an entity as the subject of the multimodal message.

The present invention relates to an improved method of providing identification of a user or authentication of a user's identity. More particularly, the present invention relates to an improved method of providing identification of a user or authentication of a user's identity using conditional behavioural biometrics. The present invention seeks to provide an enhanced method of authenticating and/or identifying a user identity using conditional behavioural biometrics. According to a first aspect of the present invention, there is provided a method of generating a user profile for use in identifying and/or authenticating a user on a device, the device equipped with one or more sensors, the method comprising: generating a set of data points from sensory data collected by the one or more sensors; clustering the set of data points to produce a set of data clusters; developing a first classifier for the data clusters, the first classifier being operable to assign a further data point derived from a further user interaction with the computing device to one of the data clusters; and developing one or more further classifiers for at least one of the data clusters, the further classifier operable to identify and/or authenticate a user identity based on the further data point.

A method for training a model based on multi-modal data joint learning, includes: obtaining multi-modal data; in which the multi-modal data include at least one type of single-modal data and at least one type of Pair multi-modal data; inputting the single-modal data and the Pair multi-modal data into a decoupling attention Transformer network model to generate respectively Token semantic representation features and cross-modal semantic representation features; and training the decoupling attention Transformer network model based on the Token semantic representation features and the cross-modal semantic representation features.

An electronic communications method, includes receiving, by a computing device, first electronic information associated with generated a graphical feature in a graphical user interface. The electronic communications method further includes generating, by the computing device, the graphical feature. The electronic communications method further includes sending, by the computing device, the graphical feature to another computing device. The electronic communications method further receiving, by the computing device, second electronic information to classify the graphical feature as public information. The electronic communications method further includes sending, by the computing device, the graphical feature to a third computing device based on the classification of the graphical feature as public information.

A method of verifying multi-modal medical data is proposed. The method comprises: accessing multi-modal medical data of a subject, the multi-modal medical data comprising a medical image of a specimen slide, wherein a specimen in the specimen slide was collected from the subject; generating a prediction pertaining to a biological attribute of the medical image based on the medical image; determining a degree of consistency between the biological attribute of the medical image and other modalities of the multi-modal medical data; and outputting, based on the degree of consistency, an indication of whether the multi-modal medical data contain inconsistency.

A method for training a machine learning system, in which image data are fed into a machine learning system with processing of at least a part of the image data by the machine learning system. The method includes synthetic generation of at least a part of at least one depth map that includes a plurality of depth information values. The at least one depth map is fed into the machine learning system with processing of at least a part of the depth information values of the at least one depth map. The machine learning system is then trained based on the processed image data and based on the processed depth information values of the at least one depth map, with adaptation of a parameter value of at least one parameter of the machine learning system, the adapted parameter value influencing an interpretation of input data by the machine learning system.