A method is disclosed for generating a corrective film pattern for reducing wafer bow in a semiconductor wafer fabrication process. The method inputs to a neural network a wafer bow signature for a predetermined semiconductor fabrication step. The neural network generates from the input a corrective film pattern corresponding to the wafer bow signature. The neural network is trained with a training dataset of wafer shape transformations and corresponding corrective film patterns.

A Generative Adversarial Network is used to train and/or tune a model used to analyze data in a database or data stream. The Generative Adversarial Network intermittently trains or tunes the model as the database is actively ingesting data and/or while the data stream is streaming. This intermittent refreshing of the model, performed by the Generative Adversarial Network, is sometimes described as “dynamic” or “dynamical.” Analytics type software is queried in order to perform normalization and/or model training.

During a training phase, a first machine learning system is trained using actual data, such as multimodal images of a hand, to generate synthetic image data. During training, the first system determines latent vector spaces associated with identity, appearance, and so forth. During a generation phase, latent vectors from the latent vector spaces are generated and used as input to the first machine learning system to generate candidate synthetic image data. The candidate image data is assessed to determine suitability for inclusion into a set of synthetic image data that may be used for subsequent use in training a second machine learning system to recognize an identity of a hand presented by a user. For example, the candidate synthetic image data is compared to previously generated synthetic image data to avoid duplicative synthetic identities. The second machine learning system is then trained using the approved candidate synthetic image data.

Computer systems and associated methods are disclosed to implement a model development environment (MDE) that allows a team of users to perform iterative model experiments to develop machine learning (ML) media models. In embodiments, the MDE implements a media data management interface that allows users to annotate and manage training data for models. In embodiments, the MDE implements a model experimentation interface that allows users to configure and run model experiments, which include a training run and a test run of a model. In embodiments, the MDE implements a model diagnosis interface that displays the model's performance metrics and allows users to visually inspect media samples that were used during the model experiment to determine corrective actions to improve model performance for later iterations of experiments. In embodiments, the MDE allows different types of users to collaborate on a series of model experiments to build an optimal media model.

A multimodal perception system for an autonomous vehicle includes a first sensor that is one of a video, RADAR, LIDAR, or ultrasound sensor, and a controller. The controller may be configured to, receive a first signal from a first sensor, a second signal from a second sensor, and a third signal from a third sensor, extract a first feature vector from the first signal, extract a second feature vector from the second signal, extract a third feature vector from the third signal, determine an odd-one-out vector from the first, second, and third feature vectors via an odd-one-out network of a machine learning network, based on inconsistent modality prediction, fuse the first, second, and third feature vectors and odd-one-out vector into a fused feature vector, output the fused feature vector, and control the autonomous vehicle based on the fused feature vector.

A computer-implemented method for training a machine-learning network. The method includes receiving an input data from a sensor, wherein the input data is indicative of image, radar, sonar, or sound information, generating an input data set utilizing the input data, wherein the input data set includes perturbed data, sending the input data set to a robustifier, wherein the robustifier is configured to clean the input data set by removing perturbations associated with the input data set to create a modified input data set, sending the modified input data set to a pretrained machine learning task, training the robustifier to obtain a trained robustifier utilizing the modified input data set, and in response to convergence of the trained robustifier to a first threshold, output the trained robustifier.

A computer-implemented system and method for training an auomated agent are disclosed. An example system includes: a communication interface; at least one processor; memory in communication with said at least one processor; software code stored in said memory, which when executed causes said system to: instantiate an automated agent that maintains a reinforcement learning neural network and generates, according to outputs of said reinforcement learning neural network, signals for communicating task requests; receive a plurality of states and a plurality of actions for the automated agent; initialize a learning table Q for the automated agent based on the plurality of states and the plurality of actions; compute a plurality of updated learning tables based on the initialized learning table Q using a utility function, the utility function comprising a monotonically increasing concave function; and generate an averaged learning table Q′ based on the plurality of updated learning tables.

An example method for training a machine learning algorithm (MLA) to control a first aircraft in an environment that comprises the first aircraft and a second aircraft can involve: determining a first-aircraft action for the first aircraft to take within the environment; sending the first-aircraft action to a simulated environment; generating and sending to both the simulated environment and the MLA, randomly-sampled values for each of a set of parameters of the second aircraft different from predetermined fixed values for the set of parameters; receiving an observation of the simulated environment and a reward signal at the MLA, the observation including information about the simulated environment after the first aircraft has taken the first-aircraft action and the second aircraft has taken a second-aircraft action based on the randomly-sampled values; and updating the MLA based on the observation of the simulated environment, the reward signal, and the randomly-sampled values.

Apparatus and methods for a smart glasses device are provided. The smart glasses device may execute a prediction model on video data captured by the smart glasses device to retrain the prediction model. The prediction model may be retrained in response to a data synchronization between an output of the prediction model and a gesture captured by the smart glasses device.

A method of deriving a financial conscientiousness score from visual choices, using a machine learning model that is trained at a server is provided. The method includes (i) obtaining visual selection as an input from the user that is selected from one or more different visual choices that are displayed to the user, (ii) determining trait scores for a subset of traits based on the points that are attributed to the visual choices, (iii) training, a machine learning model by correlating the trait scores for the subset of traits with a previous credit history or a loan repayment record of the user to obtain a trained machine learning model, and (iv) determining, using the trained machine learning model, a financial conscientiousness score based on the trait scores for the subset of traits.