Abstract: A system includes a housing, sensors, actuators, and a processing device. The housing is formed of material that is soft, flexible, stretchable, deformable, or any combination of thereof. Each sensor is disposed on a surface of the housing, disposed at least partially inside the housing, or positioned spaced apart from the housing, and are configured to generate data associated with physical properties of the housing an environment external to the housing. The processing device is configured to implement a neural network having one or more inputs and one or more outputs. The inputs include the data associated with the physical properties of the housing and the data associated with the environment external to the housing. The outputs are based on the data associated the physical properties of the housing and the data associated with the environment external to the housing.

Abstract: A self-supervised machine-learning system identifies whether an intermittent signal is present. The system includes a receiver, an encoding neural network, a decoding neural network, and a gating neural network. The receiver detects radiation and from the detected radiation generates a sampled sequence including sampled values describing the intermittent signal and noise. The encoding neural network is trained to compress each window over the sampled sequence into a respective context vector having a fixed dimension less than an incoming dimension of the window. The decoding neural network is trained to decompress the respective context vector for each window into an interim sequence describing the intermittent signal while suppressing the noise. The gating neural network is trained to produce a confidence sequence from a sigmoidal output based on the interim sequence.

Abstract: Static parameters of a software container are identified that relate to metadata of the software container itself. The software container is assigned to a selected runtime environment based on the static parameters using a first machine learning model. Runtime parameters for the software container are identified by analyzing the software container at runtime. The runtime parameters relate to operations that the software container requires during runtime. Using a second machine learning model, it is determined whether the selected runtime environment matches the runtime parameters. Where the runtime environment matches, the software container continues to run in this environment. Where the runtime environment does not match, the software container is run in a different runtime environment that matches both the static and runtime parameters.

Abstract: The present disclosure generally relates to apparatus, software and methods for detecting anomalous elements in data. For example, the data can be any time series, such as but not limited to radio frequency data, temperature data, stock data, or production data. Each type of data may be susceptible to repeating phenomena that produce recognizable features of anomalous elements. In some embodiments, the features can be characterized as known patterns and used to train a machine learning model via supervised learning to recognize those features in a new data series.

Abstract: Techniques for learning and using content type embeddings. The content type embeddings have the useful property that a distance in an embedding space between two content type embeddings corresponds to a semantic similarity between the two content types represented by the two content type embeddings. The closer the distance in the space, the more the two content types are semantically similar. The farther the distance in the space, the less the two content types are semantically similar. The learned content type embeddings can be used in a content suggestion system as machine learning features to improve content suggestions to end-users.

Abstract: Heterogeneous monitoring nodes may each generate a series of monitoring node values over time associated with operation of an industrial asset. An offline abnormal state detection model creation computer may receive the series of monitoring node values and perform a feature extraction process using a multi-modal, multi-disciplinary framework to generate an initial set of feature vectors. Then feature dimensionality reduction is performed to generate a selected feature vector subset. The model creation computer may derive digital models through a data-driven machine learning modeling method, based on input/output variables identified by domain experts or by learning from the data. The system may then automatically generate domain level features based on a difference between sensor measurements and digital model output.

Abstract: A relationship analysis device includes a parameter sample data calculation unit that calculates sample data for parameters for a simulator that receives inputs of data of a first type and outputs data of a second type, calculating sample data; a second type sample data acquisition unit that inputs, to the simulator, observation data and sample data, and obtains sample data of the second type; and a parameter value determination unit that calculates a weight for sample data based on the difference between observation data of the second type and the sample data of the second type, and based on the relationship between a first distribution that the observation data of the first type followed and a second distribution being a distribution of the data of the first type, and calculates a value for the parameters using the calculated weight.

Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a at least one processor to perform operations to implement a neural network and compute logic to accelerate neural network computations.

Abstract: In some embodiments, an application receives a request to execute a convolutional neural network model. The application determines the computational complexity requirement for the neural network based on the computing resource available on the device. The application further determines the architecture of the convolutional neural network model by determining the locations of down-sampling layers within the convolutional neural network model based on the computational complexity requirement. The application reconfigures the architecture of the convolutional neural network model by moving the down-sampling layers to the determined locations and executes the convolutional neural network model to generate output results.

Abstract: In accordance with some embodiments, systems, methods, and media for estimating compensatory reserve and predicting hemodynamic decompensation using physiological data are provided. In some embodiments, a system for estimating compensatory reserve is provided, the system comprising: a processor programmed to: receive a blood pressure waveform of a subject; generate a first sample of the blood pressure waveform with a first duration; provide the sample as input to a trained CNN that was trained using samples of the first duration from blood pressure waveforms recorded from subjects while decreasing the subject's central blood volume, each sample being associated with a compensatory reserve metric; receive, from the trained CNN, a first compensatory reserve metric based on the first sample; and cause information indicative of remaining compensatory reserve to be presented.

Abstract: Disclosed are systems and methods to incrementally train neural networks. Incrementally training the neural networks can include defining a probability distribution of labeled training examples from a training sample pool, generating a first training set based off the probability distribution, training the neural network with the first training set, adding at least one additional training sample to the training sample pool, generating a second training set, and training the neural network with the second training set. The incremental training can be recursive for additional training sets until a decision to end the recursion is made.

Abstract: Embodiments are directed towards a hardware accelerator engine that supports efficient mapping of convolutional stages of deep neural network algorithms. The hardware accelerator engine includes a plurality of convolution accelerators, and each one of the plurality of convolution accelerators includes a kernel buffer, a feature line buffer, and a plurality of multiply-accumulate (MAC) units. The MAC units are arranged to multiply and accumulate data received from both the kernel buffer and the feature line buffer. The hardware accelerator engine also includes at least one input bus coupled to an output bus port of a stream switch, at least one output bus coupled to an input bus port of the stream switch, or at least one input bus and at least one output bus hard wired to respective output bus and input bus ports of the stream switch.

Abstract: Transfer learning in machine learning can include receiving a machine learning model. Target domain training data for reprogramming the machine learning model using transfer learning can be received. The target domain training data can be transformed by performing a transformation function on the target domain training data. Output labels of the machine learning model can be mapped to target labels associated with the target domain training data. The transformation function can be trained by optimizing a parameter of the transformation function. The machine learning model can be reprogrammed based on input data transformed by the transformation function and a mapping of the output labels to target labels.

Abstract: A method, system and computer program product, the method comprising: creating a model representing underperforming cases; from a case collection having a total performance, and which comprises for each of a multiplicity of records: a value for each feature from a collection of features, a ground truth label and a prediction of a machine learning (ML) engine, obtaining one or more features; dividing the records into groups, based on values of the features in each record; for one group of the groups, calculating a performance parameter of the ML engine over the portion of the records associated with the group; subject to the performance parameter of the group being below the total performance in at least a predetermined threshold: determining a characteristic for the group; adding the characteristic of the group to the model; and providing the model to a user, thus indicating under-performing parts of the test collection.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting an action to be performed by a reinforcement learning agent interacting with an environment. A current observation characterizing a current state of the environment is received. For each action in a set of multiple actions that can be performed by the agent to interact with the environment, a probability distribution is determined over possible Q returns for the action-current observation pair. For each action, a measure of central tendency of the possible Q returns with respect to the probability distributions for the action-current observation pair is determined. An action to be performed by the agent in response to the current observation is selected using the measures of central tendency.

Abstract: A learning mechanism with partially-labeled web images is provided while correcting the noise labels during the learning. Specifically, the mechanism employs a momentum prototype that represents common characteristics of a specific class. One training objective is to minimize the difference between the normalized embedding of a training image sample and the momentum prototype of the corresponding class. Meanwhile, during the training process, the momentum prototype is used to generate a pseudo label for the training image sample, which can then be used to identify and remove out of distribution (OOD) samples to correct the noisy labels from the original partially-labeled training images. The momentum prototype for each class is in turn constantly updated based on the embeddings of new training samples and their pseudo labels.

Abstract: A method for a determinantal Point Process-based prediction includes obtaining, using a hardware processor, a training data set stored on one or more computer readable storage mediums operably coupled to the hardware processor, training an asymmetric kernel of a Determinantal Point Process (DPP) from a training data set by calculating an inverse matrix of a sum of the asymmetric kernel and an identity matrix in a recursive manner to reduce time and computational resources utilized, and determining a prediction model by training the asymmetric kernel as at least part of a prediction model to make a prediction.

Abstract: This learning device provides a learned model to an adjuster including the learned model learned to output a predetermined compensation amount to a controller based on parameters of an object to be processed, in a system including the controller outputting a command value obtained by compensating a target value based on a compensation amount; and a control object performing a predetermined process on the object and outputting a control variable as a response to the command value. The learning device includes: a learning part generating candidate compensation amounts based on operation data including a target value, command value and control variable, learning with the generated candidate compensation amounts and the parameters of the object as teacher data, and generating or updating the learned model; and a setting part providing, to the adjuster, the generated or updated learned model.

Abstract: Methods, systems and computer program products for determining recommended parameters for use in generating a word embedding model are provided. Aspects include storing a plurality of meaningful test cases. Each meaningful test case includes a test data profile and one or more test model parameters used to create a word embedding model that has been classified as yielding meaningful results. Aspects include receiving a production data set to be used in generating a new word embedding model. The production data set includes data stored in a relational database having a plurality of columns and a plurality of rows. Aspects include generating a data profile associated with the production data set. Aspects include generating a recommendation for one or more production model parameters for use in building a word embedding model based on the data profile associated with the production data set and the plurality of meaningful test cases.

Abstract: There is a need for solutions that classification solutions in hierarchical prediction domains. This need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and unstructured fusion machine learning. In one example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions. Afterward, the structure-based predictions and non-structure-based predictions are processed in accordance with an unstructured fusion model to generate one or more unstructured-fused predictions.