Abstract: Obtain, using at least one hardware processor, data characterizing a physical system governed by a physical conservation law. Apply, using the at least one hardware processor, contrastive learning to the data to automatically capture system invariants of the physical system. Employ, using the at least one hardware processor, a neural projection layer to guarantee that a corresponding dynamic machine learning model preserves the captured system invariants. Optionally, predict performance of the physical system using the corresponding dynamic machine learning model.

Abstract: Generative modeling from phylogenetic data is provided. The method comprises creating a multi-sequence alignment (MSA) based on a nucleic acid or protein sequence and generating a phylogenetic tree based on the MSA. The phylogenetic tree is fed into a number of machine learning models, which generate vector representations of the nucleic acid or protein sequences based on the phylogenetic tree. The machine learning models generate from the vector representation predicted nucleic acid or protein sequences for at least one of an evolution sequence, regression sequence, or sibling sequences of nucleic acids or proteins according to the phylogenetic tree.

Abstract: In example aspects of this disclosure, a method includes generating, by one or more computing devices, a parametric model that expresses condition states for each of a plurality of assets, and the probability of the assets transitioning between the condition states; generating, by the one or more computing devices, stochastic degradation predictions of a group of the assets, based on the condition states and the probability of transitioning between the condition states for at least some of the assets; and generating, by the one or more computing devices, a maintenance schedule based on: the stochastic degradation predictions of the group of the assets, costs of corrective maintenance for assets in a failed state, and costs of scheduled maintenance for the assets.

Abstract: Dynamic control of a production process of a manufacturing system is facilitated, where the control process includes receiving runtime input data for multiple input variables of the production process. The production process is represented, at least in part, by a physics-based expression, with at least one term of the physics-based expression being a function of two or more input variables of the production process. The control process includes determining coefficient and bias terms for a dynamic linear model connecting the multiple input variables and an output of the production process, where the terms are based, at least in part, on the input variables. The dynamic linear model and determined coefficient and bias terms are provided in an optimization model to generate a regression-optimization model which determines an optimized value of a control variable for the production process, which is used in facilitating control of the production process.

Abstract: One or more computer processors create a fully convolution network (FCN) comprising a plurality of 1×1 convolutions. The one or more computer processors append linear mapping layer (LM) to created FCN. The one or more computer processors capture a plurality of features utilizing multi-scale dilated convolutional kernels from the linear mapped FCN (LM-FCN). The one or more computer processors apply an average pool layer to the captured plurality of features along a temporal axis of a dilated convolutional kernel within the LM-FCN. The one or more computer processors predict a classification for subsequent time-series data utilizing the pooled plurality of features.

Abstract: A prediction system may identify a first set of features of training data and a second set of features of the training data. The prediction system may train a deep learning model using the training data. Training the deep learning model may comprise training a first function to determine a relationship between the first set of features and the second set of features. Training the deep learning model may further comprise training a second function to determine a relationship between missing data of a first period of time and complete data of a second period of time that follows the first period of time. The prediction system may generate imputation time series data and forecasted time series data using the trained deep learning model. The imputation time series data is generated based on an imputation task and the forecasted time series data is generated based on a forecasting task.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process for privacy-enhanced machine learning and inference. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a processing component that generates an access rule that modifies access to first data of a graph database, wherein the first data comprises first party information identified as private, a sampling component that executes a random walk for sampling a first graph of the graph database while employing the access rule, wherein the first graph comprises the first data, and an inference component that, based on the sampling, generates a prediction in response to a query, wherein the inference component avoids directly exposing the first party information in the prediction.

Abstract: Providing a trained reinforcement learning (RL) model by formulating a decision process problem for the RL model, defining at least one of a logarithmic loss function for the RL model and defining an initiation point for the RL model according to an optimized spectral norm of the RL model, training the system according to the logarithmic loss function or from the initiation point, and providing the trained RL model.

Abstract: A method, computer program product and system are provided for feature engineering and synthetic data generation. A processor retrieves a plurality of data tables, where the plurality of data tables are heterogeneous in format and content. A processor trains a variational auto-encoder (VAE) model on the plurality of data tables. A processor receives an input data table. A processor generates a synthetic data table based on the input data table and the trained VAE model.

Abstract: Using a first dataset of labeled data, a model is trained by adjusting a feature extractor parameter, a classifier parameter, and a discriminator parameter of the model. Using the discriminator parameter and a parametric function of the feature extractor parameter, a plurality of samples of a dataset of unlabeled data is scored. A subset of the scored plurality of samples is selected for labeling. Responsive to receiving a label of each of the selected subset of the scored plurality of samples, the first dataset of labeled data is augmented with the selected subset of the scored plurality of samples and the label of each of the selected subset of the scored plurality of samples. Using the augmented dataset of labeled data, the model is retrained. The retraining comprises further adjusting the feature extractor parameter, the classifier parameter, and the discriminator parameter of the model.

Abstract: A maintenance solution pipeline is automatically selected from a plurality of maintenance solution pipelines, based on obtained information. The maintenance solution pipeline is to be used in providing a physical asset maintenance solution for a plurality of physical assets. Code and model rendering for the maintenance solution pipeline automatically selected is initiated. Output from an artificial intelligence process is obtained. The output includes an automatically generated risk estimation relating to one or more conditions of at least one physical asset of the plurality of physical assets. Code and model rendering for the maintenance solution pipeline is re-initiated, based on the output from the artificial intelligence process. The maintenance solution pipeline automatically selected is reused.

Abstract: In aspects of the disclosure, a method comprises training, by a computing system, a dynamics model of a cooperative multi-agent reinforcement learning (c-MARL) environment. The method further comprises processing, by the computing system, a perturbation optimizer to generate a state perturbation of the c-MARL environment, based on the dynamics model. The method further comprises selecting one or more agents of the c-MARL system as having enhanced vulnerability. The method further comprises attacking, by the computing system, the c-MARL system based on the state perturbation and the selected one or more agents.

Abstract: Select an initial weight vector for a convex optimization sub-problem associated with a neural network having a non-convex network architecture loss surface. With at least one processor, approximate a solution to the convex optimization sub-problem that obtains a search direction, to learn a common classifier from training data. With the at least one processor, update the initial weight vector by subtracting the approximate solution to the convex optimization sub-problem times a first learning rate. With the at least one processor, repeat the approximating and updating steps, for a plurality of iterations, with the updated weight vector from a given one of the iterations taken as the initial weight vector for a next one of the iterations, to obtain a final weight vector for the neural network, until convergence to a global minimum is achieved, to implement the common classifier.

Abstract: In example aspects of this disclosure, a method includes generating, by one or more computing devices, a parametric model that expresses condition states for each of a plurality of assets, and the probability of the assets transitioning between the condition states; generating, by the one or more computing devices, stochastic degradation predictions of a group of the assets, based on the condition states and the probability of transitioning between the condition states for at least some of the assets; and generating, by the one or more computing devices, a maintenance schedule based on: the stochastic degradation predictions of the group of the assets, costs of corrective maintenance for assets in a failed state, and costs of scheduled maintenance for the assets.

Abstract: Methods, systems, and computer program products for a decision-improvement framework are provided herein. A computer-implemented method includes obtaining regression functions that predict an output of processes of a physical system based on inputs received at each process; automatically generating one or more constraints and one or more objective functions for a model for the physical system based on the regression functions and a representation of the physical system, where the representation specifies relationships between at least a portion of the processes; identifying a set of parameter values for controlling the physical system based on the model; generating a score, for the set of parameter values, based on a predicted improvement of the physical system relative to historical performance of the physical system; and in response to the generated score satisfying a threshold, causing the physical system to be configured in accordance with the set of parameter values.

Abstract: A computer implemented method for certifying robustness of image classification in a neural network is provided. The method includes initializing a neural network model. The neural network model includes a problem space and a decision boundary. A processor receives a data set of images, image labels, and a perturbation schedule. Images are drawn from the data set in the problem space. A distance from the decision boundary is determined for the images in the problem space. A re-weighting value is applied to the images. A modified perturbation magnitude is applied to the images. A total loss function for the images in the problem space is determined using the re-weighting value. A confidence level of the classification of the images in the data set is evaluated for certifiable robustness.

Abstract: An index sequence specifying an index of training data corresponding to a component of a cost function is generated. A first model parameter in the set of model parameters is set to an initial value. Using the index sequence, a neural network model comprising a set of weights is trained. As part of the training, using the index sequence, a learning rate, and a set of gradients, a subset of the set of model parameters is updated. As part of the training, a momentum term is set. As part of the training, using the momentum term as the first model parameter, the updating and the setting are repeated until reaching a training completion condition. The trained neural network model is used to predict an outcome by analyzing live data.

Abstract: A method includes training, by one or more processing devices, a plurality of machine learning predictive models, thereby generating a plurality of trained machine learning predictive models. The method further includes generating, by the one or more processing devices, a solved machine learning optimization model, based at least in part on the plurality of trained machine learning predictive models. The method further includes outputting, by the one or more processing devices, one or more control input and predicted outputs based at least in part on the solved machine learning optimization model.

Abstract: In unsupervised interpretable machine learning, one or more datasets having multiple features can be received. A machine can be trained to jointly cluster and interpret resulting clusters of the dataset by at least jointly clustering the dataset into clusters and generating hyperplanes in a multi-dimensional feature space of the dataset, where the hyperplanes separate pairs of the clusters, where a hyperplane separates a pair of clusters. Jointly clustering the dataset into clusters and generating hyperplanes can repeat until convergence, where the clustering in a subsequent iteration uses the generated hyperplanes from a previous iteration to optimize performance of the clustering. The hyperplanes can be adjusted to further improve the performance of the clustering. The clusters and interpretation of the clusters can be provided, where a cluster's interpretation is provided based on hyperplanes that construct a polytope containing the cluster.

Abstract: A method includes: receiving, by a computing device, data from sensors in a manufacturing environment; mapping, by the computing device, the data into a deep learning network; learning, by the computing device, correlations between inputs and outputs of the manufacturing environment using the data; pruning, by the computing device, the deep learning network; predicting, by the computing device and using the pruned network, an output of the pruned network from the inputs of the manufacturing environment; linearizing, by the computing device, the pruned network; optimizing, by the computing device, the output of the linearized pruned network to calculate predicted inputs for the manufacturing environment; and changing, by the computing device, operation inputs in the manufacturing environment to match the predicted inputs.