A system and method for determining a relative importance of a selected test in a plurality of tests, comprising a computational device for receiving one or more characteristics relating to an importance of the code, an importance of each of the plurality of tests, or both; and for determining the relative importance of the selected test according to said characteristics.

Disclosed herein are methods for training and deploying a predictive model for generating a prediction, e.g., patient eligibility for a CAR-T therapy. Datasets, such as open healthcare claims datasets, may be missing data. Missing data may hamper the ability to generate sufficient information needed for training a predictive model. Methods include leveraging comprehensive datasets, such as closed claims datasets, to create training examples for input into a machine learning algorithm. In various embodiments, the comprehensive dataset is modified to simulate the data missingness in the target dataset; then, the modified dataset is paired with the ground truth label derived from the comprehensive dataset to create training examples. In various embodiments, a comprehensive dataset is paired with a target dataset to create training examples. After training a predictive model on such examples, the model can be deployed to make predictions in the target dataset even in light of missing data.

The present disclosure relates to a method and an apparatus for deleting training data of a deep learning model. The trained data deleting method according to an exemplary embodiment of the present disclosure includes calculating a result value for a label allocated to data to be deleted which is included in the training data; reallocating a label of the data to be deleted by comparing the result value; generating a neutralized model obtained by neutralizing the deep learning model with the data to be deleted and a reallocated label of the data to be deleted as inputs; and training the neutral model based on retrained data which is training data, excluding the data to be deleted, among the trained data.

Aspects of the invention include detecting an error alert from a target computer system. In response to detecting the error alert, performance data is then retrieved from the target computer system. A gated recurrent unit (GRU) neural network is used to generate a prediction of a root cause of the error alert based on the performance data. The weights of a reset gate of the GRU neural network are adjusted based on received feedback of the prediction.

An image sensor, electronic device and method thereof that performs on-sensor multiresolution deep neural network (DNN) processing, such as for gesture recognition. The image data is transformed into first resolution type image data and second resolution type image data. Based on detecting the first resolution type image data includes a predetermined object, processing the second resolution type image data using the second resolution type image data as input into the second DNN.

A non-transitory computer readable storage medium store one or more programs including instructions causing an electronic device to receive, while a visual object is in a first state among a plurality of states, a user input for switching a state of the visual object to a second state among the plurality of states; provide data regarding the user input as input data to a neural network for training of the neural network; identify a third state among the plurality of states, wherein the third state is an intermediate state for switching the first state to the second state; obtain, from the neural network, data regarding the third state as output data for the data regarding the user input; determine a compensation value for the data regarding the third state; and train, by providing the data regarding the compensation value to the neural network, the neural network.

Various embodiments provide for deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization greatly reduces complexity and power consumption in the receivers, but makes accurate channel estimation and data detection difficult. This is particularly true for OFDM waveforms, which have high peak-to average (signal power) ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), various embodiments use novel generative supervised deep neural networks (DNNs) that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver specifically, an autoencoder jointly learns a precoder and decoder for data symbol detection.

A system includes a data collection engine, a plurality of items including radio-frequency identification chips, a plurality of third party data and insight sources, a plurality of interfaces, client devices, a server and method thereof for preventing suicide. The server includes trained machine learning models, business logic and attributes of a plurality of patient events. The data collection engine sends attributes of new patient events to the server. The server can predict an adverse event risk of the new patient events based upon the attributes of the new patient events utilizing the trained machine learning models.

Techniques and methods for training and/or using a machine learned model that identifies unsafe events. For instance, computing device(s) may receive input data, such as vehicle data generated by one or more vehicles and/or simulation data representing a simulated environment. The computing device(s) may then analyze features represented by the input data using one or more criteria in order to identify potential unsafe events represented by the input data. Additionally, the computing device(s) may receive ground truth data classifying the identified events as unsafe events or safe events. The computing device(s) may then train the machine learned model using at least the input data representing the unsafe events and the classifications. Next, when the computing device(s) and/or vehicles receive input data, the computing device(s) and/or vehicles may use the machine learned model to determine if the input data represents unsafe events.

The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method can include receiving, by a processing device, image data having one or more image frames indicative of a state of a meal preparation area. The method may further include, determining, based on the image data, a first feature characterization of a first meal preparation item associated with the state of the meal preparation area. The method may further include determining that the first feature characterization does not meet object classification criteria for a set of object classifications. The method may further include causing a notification indicating the first meal preparation item and one of an object classification or a classification status corresponding to the first meal preparation item on a graphical user interface (GUI).