Apparatuses, systems, and techniques to detect loops in neural network graphs. In at least one embodiment, one or more loops are detected within one or more graphs corresponding to one or more neural networks.

Disclosed are embodiments for improving training data for machine learning (ML) models. In an embodiment, a method is disclosed where an augmentation engine receives a seed example, the seed example stored in a seed training data set; generates an encoded seed example of the seed example using an encoder; inputs the encoded seed example into a machine learning model and receives a candidate example generated by the machine learning model; determines that the candidate example is similar to the encoded seed example; and augments the seed training data set with the candidate example.

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 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.

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.

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

Provided is a three-dimensional vertical memory device including: a semiconductor substrate, a vertical columnar channel region provided on the semiconductor substrate and having a void of a predetermined size therein; a source electrode and a drain electrode spaced apart from each other with the channel region interposed therebetween; and a gate stack formed on the channel region.

Methods and systems to provide a form of probabilistic labeling to associate an outage with a disturbance, which could itself be either known based on the available data or unknown. In the latter case, labeling is especially challenging, as it necessitates the discovery of the disturbance. One approach incorporates a statistical change-point analysis to time-series events that correspond to service tickets in the relevant geographic sub-regions. The method is calibrated to separate the regular periods from the environmental disturbance periods, under the assumption that disturbances significantly increase the rate of loss-causing events. To obtain the probability that a given loss-causing event is related to an environmental disturbance, the method leverages the difference between the rate of events expected in the absence of any disturbances (baseline) and the rate of actually observed events. In the analysis, the local disturbances are identified and estimators of their duration and magnitude are provided.

Systems as described herein generate an outstanding check alert. An alert generating server may receive transaction records associated with a plurality of checking accounts. The alert generating server may user a first machine learning classifier to determine a transaction pattern indicating a merchant has failed to process outstanding checks for a period of time. The alert generating server may receive sequential check information comprising at least one missing check number associated with a particular checking account. The alert generating server may user a second machine learning classifier to determine at least one outstanding check associated with the particular checking account. The alert generating server may send an alert indicating the at least one outstanding check to a user device.

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.