A computerized method for transforming an interactive graphical user interface according to machine learning includes selecting a persona, loading a data structure associated with the selected persona, and generating the interactive graphical user interface. The method includes, in response to a user selecting a first selectable element, inputting a first set of explanatory variables to a first trained machine learning model to generate a first metric, and transforming the user interface according to the selected persona and the first metric. The method includes, in response to the user selecting a second selectable element, inputting a second set of explanatory variables to a second trained machine learning model to generate a second metric, and transforming the user interface according to the selected persona and the second metric. In various implementations, first metric is a first probability of the persona being approved for a first prior authorization prescription.

Methods and systems are provided for detecting and responding to dangerous road users. In some aspects, a process can include steps for receiving sensor data of a detected object from an autonomous vehicle, determining whether the detected object is exhibiting a dangerous attribute, generating output data based on the determining of whether the detected object is exhibiting the dangerous attribute, and updating a machine learning model based on the output data relating to the dangerous attribute.

A system includes a processor and a non-transitory computer-readable medium containing instructions that when executed by the processor causes the processor to perform operations comprising displaying a prompt to a user of a mobile device on a display of a mobile device to capture an image representing at least a portion of a mouth of the user using a rear-facing camera of the mobile device, where the rear-facing camera is on an opposite side of the mobile device including the display. The operations further comprise controlling the rear-facing camera to enable the rear-facing camera to capture the image, receiving the image, and outputting, user feedback based on the image, where the user feedback is outputted on the display that is on the opposite side of the mobile device than the rear-facing camera.

A method for application of a deep learning neural network (NN) for predicting the probability distribution of a biological phenotype does not require any assumption or prior knowledge of the probability distributions. The NN may be a recurrent neural network (RNN) or a long short-term memory (LSTM) network. The NN includes a loss function, which is trained on limited observations, as low as one observation, which is obtained from a large data set related to a biological system. The NN with the trained loss function is capable of calculating if readings that are outside of the mean for the data set are inherent to the biological system or are outlier readings. The output of the method is a continuous probability distribution of the biological phenotypes for each input parameter or set of parameters from the biological data set.

System and methods enable vehicles to make ethical/empathetic driving decisions by using deep learning aided location intelligence. The systems and methods identify moral islands/complex driving scenarios where a complex ethical decision is required. A Generative Adversarial Network (GAN) is used to generate synthetic training data to capture varied ethically complex driving situations. Embodiments train a deep learning model (ETHNET) that is configured to output one or more driving decisions to be taken when a vehicle comes across an ethically complex driving situations in the real world.

The present invention relates to metalearning of deep neural network (DNN) architectures and hyperparameters. Precisely, the present system and method utilizes Evolutionary Population-Based Based Training (EPBT) that interleaves the training of a DNN's weights with the metalearning of loss functions. They are parameterized using multivariate Taylor expansions that EPBT can directly optimize. Further, EPBT based system and method uses a quality-diversity heuristic called Novelty Pulsation as well as knowledge distillation to prevent overfitting during training. The discovered hyperparameters adapt to the training process and serve to regularize the learning task by discouraging overfitting to the labels. EPBT thus demonstrates a practical instantiation of regularization metalearning based on simultaneous training.

Provided is an encoding method according to various example embodiments and an encoder performing the method. The encoding method includes outputting a linear prediction(LP) coefficients bitstream and a residual signal by performing a linear prediction analysis on an input signal, outputting a first latent signal obtained by encoding a periodic component of the residual signal, using a first neural network module, outputting a first bitstream obtained by quantizing the first latent signal, using a quantization module, outputting a second latent signal obtained by encoding an aperiodic component of the residual signal, using the first neural network module, and outputting a second bitstream obtained by quantizing the second latent signal, using the quantization module, wherein the aperiodic component of the residual signal is calculated based on a periodic component of the residual signal decoded from the quantized first latent signal output by de-quantizing the first bitstream.

A data processing array comprises a plurality of modules, each with a memory, positioned in an array of rows and columns interconnected by a pooling chain that carries data to and receives data from selected ones or groups of the modules. Each modules can also have light modulator elements for transmitting data as light signals and a light sensor for receiving data in the form of modulated light. Pooling switches in the pooling chain between modules open and close the pooling chain lines for selecting and grouping modules. Analog data lines separate from the pooling chain can also carry data to and from the modules. Pooling control lines connected to the switches turn the switches on and off for the selecting and grouping of modules. Module control lines, also separate from the pooling chain, connected to the modules enable various data input, output, and processing by the memory or other components in the module.

The present invention discloses an indoor Electronic Traveling Aid (ETA) system for blind and visually impaired (BVI) people. The system comprises a headband, intuitive tactile display with myographic (EMG) feedback, controller, and server-based methods corresponding to three operation modalities. In 1.sup.st modality, sighted users mark routes, map navigational directions, and create semantic comments for BVIs. This information of routes is continuously collected and estimated in ETA servers. In the 2.sup.nd modality, BVIs choose the routes from servers, thereby, are supplied with real-time navigational guidance. Also, an EMG interface is used, where the user's facial muscles are enabled is to send commands to the ETA system. In the 3.sup.rd modality, BVIs receive real-time audio guidance in complex or unforeseen situations: ETA provides a crowd-assisted interface and real-time sensory (e.g., video) data, where crowd-assistants analyze the situation and help the BVI to navigate.

A method and system are provided which facilitate construction of an ensemble of neural network kernel classifiers. The system divides a training set into partitions. The system trains, based on the training set, a first neural network encoder to output a first set of features, and trains, based on each respective partition of the training set, a second neural network encoder to output a second set of features. The system generates, for each respective partition, based on the first and second set of features, kernel models which output a third set of features. The system classifies, by a classification model, the training set based on the third set of features. The generated kernel models for each respective partition and the classification model comprise the ensemble of neural network kernel classifiers. The system predicts a result for a testing data object based on the ensemble of neural network kernel classifiers.