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: A computer-program product storing instructions which, when executed by a computer, cause the computer to receive an input data from a sensor, generate a training data set utilizing the input data, wherein the training data set is created by creating one or more copies of the input data and adding noise to the one or more copies, send the training data set to a diffusion model, wherein the diffusion model is configured to reconstruct and purify the training data set by removing noise associated with the input data and reconstructing the one or more copies of the training data set to create a modified input data set, send the modified input data set to a fixed classifier, and output a classification associated with the input data in response to a majority vote of the classification obtained by the fixed classifier of the modified input data set.

Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.

Abstract: 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.

Abstract: A method of identifying an attack comprising receiving an input of one or more images, wherein the one or more images includes a patch size and size, divide the image into a first sub-image and a second sub-image, classify the first sub-image and the second sub-image, wherein classifying is accomplished via introducing a variable in a pixel location associated with the first and second sub-image, and in response to classifying the first and second sub-image and identifying an adversarial patch, output a notification indicating that the input is not certified.

Abstract: A computer-implemented system and method relate to test-time adaptation of a machine learning system from a source domain to a target domain. Sensor data is obtained from a target domain. The machine learning system generates prediction data based on the sensor data. Pseudo-reference data is generated based on a gradient of a predetermined function evaluated with the prediction data. Loss data is generated based on the pseudo-reference data and the prediction data. One or more parameters of the machine learning system is updated based on the loss data. The machine learning system is configured to perform a task in the target domain after the one or more parameters has been updated.

Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating toa plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.

Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to detect and ascribe network interruptions to specific components or nodes within the network. In an aspect, the present disclosure provides a computer-implemented method comprising: mapping a network comprising a plurality of islands that are capable of dynamically changing by splitting and/or merging of one or more islands, wherein the plurality of islands comprises a plurality of individual components; and detecting and localizing one or more local events at an individual component level as well as at an island level using a disaggregation model.

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 of estimating an accuracy of a neural network on out-of-distribution data. In-distribution accuracies of a plurality of machine learning models trained with in-distribution data are determined. The plurality of machine learning models includes a first model, and a remainder of models. In-distribution agreement is determined between (i) an output of the first machine learning model executed with an in-distribution dataset and (ii) outputs of a remainder of the plurality of machine learning models executed with the in-distribution dataset. The machine learning models are also executed with an unlabeled out-of-distribution dataset, and an out-of-distribution agreement is determined. The in-distribution agreement is compared with the out-of-distribution agreement.

Abstract: A dynamic equilibrium (DEQ) model circuit includes a first multiplier configured to receive an input, scale the input by a first weight, and output the scaled input, second multiplier configured to receive a root, scale the root by a second weight, and output the scaled root, a summation block configured to combine the scaled input, a bias input, and the scaled root and output a non-linear input, and a first non-linear function configured to receive the non-linear input and output the root, wherein the first weight and second weight are based on a trained DEQ model of a neural network.

Abstract: A machine learning (ML) method for predicting an electronic structure of an atomic system. The method includes receiving an atomic identifier and an atomic position for atoms in the atomic system; receiving a basis set including rules for forming atomic orbitals of the atomic system; forming the atomic orbitals of the atomic system; and predicting an electronic structure of the atomic system based on the atom identifier, the atom position for the atoms in the atomic system, and the atomic orbitals of the atomic system. The ML method is capable of extremely accurate and fast molecular property prediction. The ML can directly purpose basis dependent information to predict molecular electronic structure. The ML method, which may be referred to as an orbital mixer model, uses multi-layer perception (MLP) mixer layers within a simple, intuitive, and scalable architecture to achieve competitive Hamiltonian and molecular orbital energy and coefficient prediction accuracies.

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: The present disclosure provides systems and methods that may advantageously apply machine learning to detect and ascribe network interruptions to specific components or nodes within the network. In an aspect, the present disclosure provides a computer-implemented method comprising: mapping a network comprising a plurality of islands that are capable of dynamically changing by splitting and/or merging of one or more islands, wherein the plurality of islands comprises a plurality of individual components; and detecting and localizing one or more local events at an individual component level as well as at an island level using a disaggregation model.

Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to detect and ascribe network interruptions to specific components or nodes within the network. In an aspect, the present disclosure provides a computer-implemented method comprising: mapping a network comprising a plurality of islands that are capable of dynamically changing by splitting and/or merging of one or more islands, wherein the plurality of islands comprises a plurality of individual components; and detecting and localizing one or more local events at an individual component level as well as at an island level using a disaggregation model.

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 system includes a machine-learning network. The network includes an input interface configured to receive input data from a sensor. The processor is programmed to receive the input data, generate a perturbed input data set utilize the input data, wherein the perturbed input data set includes perturbations of the input data, denoise the perturbed input data set utilizing a denoiser, wherein the denoiser is configured to generate a denoised data set, send the denoised data set to both a pre-trained classifier and a rejector, wherein the pre-trained classifier is configured to classify the denoised data set and the rejector is configured to reject a classification of the denoised data set, train, utilizing the denoised input data set, the a rejector to achieve a trained rejector, and in response to obtaining the trained rejector, output an abstain classification associated with the input data, wherein the abstain classification is ignored for classification.

Abstract: A computer-program product storing instructions which, when executed by a computer, cause the computer to receive 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, generate an adversarial version of the input data, utilizing a generator, in response to the input data, create a training data set utilizing the input data and the adversarial version of the input data, determine an update direction of a meta model utilizing stochastic gradient respect with respect to an adversarial loss, and determine a cross-entropy based classification loss in response to the input data and classification utilizing a classifier, and update the meta model and the classifier in response to the cross-entropy classification loss utilizing the training data set.

Abstract: A computational method for training a classifier. The method includes receiving a training data set comprised of pairs of training input and output signals, the classifier parameterized by parameters, a class-dependent allowed perturbation for each of at least two different classes and including a first class-dependent allowed perturbation for a first class and a second class-dependent allowed perturbation for a second class, and a loss function. The method further includes partitioning the training data set into a first subset labelled with a first label and a second subset labelled with a second label. The method also includes calculating a first loss in response to the first subset and the first class-dependent allowed perturbation and a second loss calculated in response to the second subset and the second class-dependent allowed perturbation. The method also includes updating the parameters in response to the first and second losses to obtain updated parameters.

Abstract: A method for training a machine-learning network includes receiving an input data from a sensor. The input data includes a perturbation. The method also includes obtaining a worst-case bound on a classification error and loss for perturbed versions of the input data. The method also includes training a classifier, where the classifier includes a plurality of classes, including a plurality of additional abstain classes. Each additional abstain class of the plurality of additional abstain classes is determined in response to at least bounding the input data. The method also includes outputting a classification in response to the input data indicating one of the plurality of classes and outputting a trained classifier in response to exceeding a convergence threshold. The trained classifier is configured to detect at least one additional abstain class of the plurality of additional abstain classes in response to obtaining the worst-case bound.