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 method discloses receiving, at a cross-attention layer of a model, first text data describing a first object and second text data describing a first scene, wherein the first text data includes a description of a location of the first object, utilizing the model with cross-attention layers, concatenating the first text data and the second text data to generate a prompt; generating, a broadcasted location mask constructed from at least the location; generating, a broadcasted all-one matrix associated with the second text data described the first scene; computing a key matrix and a value matrix utilizing separate linear projections of the prompt; computing a query matrix utilizing linear projections; generating a broadcasted location matrix in response to concatenating the broadcasted location mask and the broadcasted all-one matrix; generating a cross-attention map utilizing the query matrix, the key matrix, and the broadcasted location matrix; and outputting a final image.

Abstract: Performing semantic segmentation in an absence of labels for one or more semantic classes is provided. One or more weak predictors are utilized to obtain label proposals of novel classes for an original dataset for which at least a subset of sematic classes are unlabeled classes. The label proposals are merged with ground truth of the original dataset to generate a merged dataset, the ground truth defining labeled classes of portions of the original dataset. A machine learning model is trained using the merged dataset. The machine learning model is utilized for performing semantic segmentation on image data.

Abstract: A wireless system includes a controller that is configured to measure a first Reference Signal Received Power (RSRP1) from a first carrier at a first time, measure a second Reference Signal Received Power (RSRP2) from a second carrier at a second time, annotate the RSRP1 to the first carrier and the RSRP2 to the second carrier, in response to the first time and the second time being within a contemporaneous period, associate the RSRP1 and RSRP2 to the contemporaneous period, create an N-Dimension vector of RSRP1 and RSRP2 at the contemporaneous period, process the N-Dimension vector via a trainable function to obtain a predicted data rate, and in response to the predicted data rate falling below a normal operating range threshold, operate the system in a low bandwidth mode.

Abstract: A method of image segmentation includes receiving one or more images, determining a loss component, for each pixel one image of the one or more images, identifying a majority class and identify a cross-entropy loss between a network output and a target, randomly selecting pixels associated with the one image and select a second set of pixels to compute a super pixel loss for each pair of pixels, summing corresponding loss associated with each pair of pixels, for each corresponding frame of the plurality of frames of the image, computing a flow loss, a negative flow loss, a contrastive optical flow loss, and a equivariant optical flow loss, computing a final loss including a weighted average of the flow loss, the cross entropy loss, the super pixel loss, and foreground loss, updating a network parameter and outputting a trained neural network.

Abstract: Methods and systems for classifying a article of manufacture are disclosed. A classifier is trained with training data including 1) a feature vector related to the article based on measurements related to the article captured at a particular station of a manufacturing process and 2) encoded time series data representing a history of measurements of articles of the same type as the article of manufacture captured at a sequence of stations of the manufacturing process prior to the particular station.

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: 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 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 wireless system includes a controller that is configured to measure a first Reference Signal Received Power (RSRP1) from a first carrier at a first time, measure a second Reference Signal Received Power (RSRP2) from a second carrier at a second time, annotate the RSRP1 to the first carrier and the RSRP2 to the second carrier, in response to the first time and the second time being within a contemporaneous period, associate the RSRP1 and RSRP2 to the contemporaneous period, create an N-Dimension vector of RSRP1 and RSRP2 at the contemporaneous period, process the N-Dimension vector via a trainable function to obtain a predicted data rate, and in response to the predicted data rate falling below a normal operating range threshold, operate the system in a low bandwidth mode.

Abstract: Performing semantic segmentation in an absence of labels for one or more semantic classes is provided. One or more weak predictors are utilized to obtain label proposals of novel classes for an original dataset for which at least a subset of sematic classes are unlabeled classes. The label proposals are merged with ground truth of the original dataset to generate a merged dataset, the ground truth defining labeled classes of portions of the original dataset. A machine learning model is trained using the merged dataset. The machine learning model is utilized for performing semantic segmentation on image data.

Abstract: A method of image segmentation includes receiving one or more images, determining a loss component, for each pixel one image of the one or more images, identifying a majority class and identify a cross-entropy loss between a network output and a target, randomly selecting pixels associated with the one image and select a second set of pixels to compute a super pixel loss for each pair of pixels, summing corresponding loss associated with each pair of pixels, for each corresponding frame of the plurality of frames of the image, computing a flow loss, a negative flow loss, a contrastive optical flow loss, and a equivariant optical flow loss, computing a final loss including a weighted average of the flow loss, the cross entropy loss, the super pixel loss, and foreground loss, updating a network parameter and outputting a trained neural network.

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: 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: 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.