Abstract: Methods and systems of using a trained machine-learning model to perform root cause analysis on a manufacturing process. A pre-trained machine learning model is provided that is trained to predict measurements of non-faulty parts. The pre-trained model is trained on training measurement data regarding physical characteristics of manufactured parts as measured by a plurality of sensors at a plurality of manufacturing stations. With the trained model, then measurement data from the sensors is received regarding the manufactured part and the stations. This new set of measurement data is back propagated through the pre-trained model to determine a magnitude of absolute gradients of the new measurement data. The root cause is then identified based on this magnitude of absolute gradients. In other embodiments the root cause is identified based on losses determined between a set of predicted measurement data of a part using the model, and actual measurement data.

Abstract: A convolutional neural network system includes a sensor and a controller, wherein the controller is configured to receive an image from the sensor, divide the image into patches, each patch of size p, extract, via a first convolutional layer, a feature map having a number of channels based on a feature detector of size p, wherein the feature detector has a stride equal to size p, refine the feature map by alternatingly applying depth-wise convolutional layers and point-wise convolutional layers to obtain a refined feature map, wherein the number of channels in the feature map and the size of the feature map remains constant throughout all operations in the refinement; and output the refined feature map.

Abstract: Methods and systems for training a machine learning model with measurement data captured during a manufacturing process. Measurement data regarding a physical characteristic of a plurality of manufactured parts is received as measured by a plurality of sensors at various manufacturing stations. A time-series dynamics machine learning model encodes the measurement data into a latent space having a plurality of nodes. Each node is associated with the measurement data of one of the manufactured parts and at one of the manufacturing stations. A batch of the measurement data can be built, the batch include a first node and a first plurality of nodes immediately connected to the first node via first edges, and measured in time earlier than the first node. A prediction machine learning model can predict measurements of a first of the manufactured parts based on the latent space of the batch of nodes.

Abstract: Methods and systems are disclosed for generating training data for a machine learning model for better performance of the model. A source image is selected from an image database, along with a target image. An image segmenter is utilized with the source image to generate a source image segmentation mask having a foreground region and a background region. The same is performed with the target image to generate a target image segmentation mask having a foreground region and a background region. Foregrounds and backgrounds of the source image and target image are determined based on the masks. The target image foreground is removed from the target image, and the source image foreground is inserted into the target image to create an augmented image having the source image foreground and the target image background. The training data for the machine learning model is updated to include this augmented image.

Abstract: A computer-implemented method for a machine learning (ML) system includes receiving a first image frame and a second frame from a sensor, wherein the first and second image frames are time series data, determining a first flow state and a first latent state of the first image frame, determining a Deep Equilibrium Model (DEQ) based fix point solution via a root finding method based on the first flow state, the first latent state, and a layer function to obtain an estimated flow and latent state, receiving a third image frame, wherein the second and third image frames are time series data, determining the fix point solution via the root finding method based on the estimated flow state, the estimated latent state, and layer function to obtain an updated flow state and updated latent state, and outputting the updated flow state.

Abstract: A convolutional neural network system includes a sensor and a controller, wherein the controller is configured to receive an image from the sensor, divide the image into patches, each patch of size p, extract, via a first convolutional layer, a feature map having a number of channels based on a feature detector of size p, wherein the feature detector has a stride equal to size p, refine the feature map by alternatingly applying depth-wise convolutional layers and point-wise convolutional layers to obtain a refined feature map, wherein the number of channels in the feature map and the size of the feature map remains constant throughout all operations in the refinement; and output the refined feature map.

Abstract: Systems and methods for operating a deep equilibrium (DEQ) model in a neural network are disclosed. DEQs solve for a fixed point of a single nonlinear layer, which enables decoupling the internal structure of the layer from how the fixed point is actually computed. This disclosure discloses that such decoupling can be exploited while substantially enhancing this fixed point computation using a custom neural solver.

Abstract: Methods and systems for inferring data to supplement an input utilizing a neural network, and training such a system, are disclosed. In embodiments, an input is received from a sensor at the neural network. Iterations of approximate probabilities can be determined based on hidden-to-hidden Markov random field (MRF) potentials, observed-to-hidden MRF potentials, and unary MRF potentials. A constant can be identified using a root-finding algorithm. The iterations can continue until convergence. The final iteration of the approximate probability can be used to supplement the input to produce an output.

Abstract: A computer-implemented method for a classification and training a neural network includes receiving input at the neural network, wherein the input includes a plurality of resolution inputs of varying resolutions, outputting a plurality of feature tensors for each corresponding resolution of the plurality of resolution inputs, fusing the plurality of feature tensors utilizing upsampling or down sampling for the vary resolutions, utilizing an equilibrium solver to identify one or more prediction vectors from the plurality of feature tensors, and outputting a loss in response to the one or more prediction vectors.

Abstract: A system for controlling a physical system via segmentation of an image includes a controller. The controller may be configured to receive an image of n pixels from a first sensor, and an annotation of the image from a second sensor, form a coupling matrix, k class vectors each of length n, and a bias coefficient based on the image and the annotation, generate n pixel vectors each of length n based on the coupling matrix, class vectors, and bias coefficient create a single segmentation vector of length n from the pixel vectors wherein each entry in the segmentation vector identifies one of the k class vectors, output the single segmentation vector; and operate the physical system based on the single segmentation vector.

Abstract: Regularized training of a Deep Equilibrium Model (DEQ) is provided. A regularization term is computed using a predefined quantity of random samples and the Jacobian matrix of the DEQ, the regularization term penalizing the spectral radius of the Jacobian matrix. The regularization term is included in an original loss function of the DEQ to form a regularized loss function. A gradient of the regularized loss function is computed with respect to model parameters of the DEQ. The gradient is used to update the model parameters.

Abstract: A computer-implemented method for training a machine-learning network, wherein the network includes receiving an input data from a sensor, wherein the input data includes data indicative of an image, wherein the sensor includes a video, radar, LiDAR, sound, sonar, ultrasonic, motion, or thermal imaging sensor, generating an adversarial version of the input data utilizing an optimizer, wherein the adversarial version of the input data utilizes a subset of the input data, parameters associated with the optimizer, and one or more perturbation tiles, determining loss function value in response to the adversarial version of the input data and a classification of the adversarial version of the input data, determining a perturbation tile in response the loss function value associated with one or more subsets of the adversarial version of the input data, and output a perturbation that includes at least the perturbation tile.

Abstract: A system for controlling a physical system via segmentation of an image includes a controller. The controller may be configured to receive an image of n pixels from a first sensor, and an annotation of the image from a second sensor, form a coupling matrix, k class vectors each of length n, and a bias coefficient based on the image and the annotation, generate n pixel vectors each of length n based on the coupling matrix, class vectors, and bias coefficient create a single segmentation vector of length n from the pixel vectors wherein each entry in the segmentation vector identifies one of the k class vectors, output the single segmentation vector; and operate the physical system based on the single segmentation vector.

Abstract: A computer-implemented method for a classification and training a neural network includes receiving input at the neural network, wherein the input includes a plurality of resolution inputs of varying resolutions, outputting a plurality of feature tensors for each corresponding resolution of the plurality of resolution inputs, fusing the plurality of feature tensors utilizing upsampling or down sampling for the vary resolutions, utilizing an equilibrium solver to identify one or more prediction vectors from the plurality of feature tensors, and outputting a loss in response to the one or more prediction vectors.

Abstract: A computational method for training a meta-learned, evolution strategy black box optimization classifier. The method includes receiving one or more training functions and one or more initial meta-learning parameters of the meta-learned, evolution strategy black box optimization classifier. The method further includes sampling a sampled objective function from the one or more training functions and an initial mean of the sampled function. The method also includes computing a set of T number of means by running the meta-learned, evolution strategy black box optimization classifier on the sampled objective function using the initial mean for T number of steps in t=1, . . . ,T. The method also includes computing a loss function from the set of T number of means. The method further includes updating the one or more initial meta-learning parameters of the meta-learned, evolution strategy black box optimization classifier in response to a characteristic of the loss function.