Abstract: A data set including identifiers with respective acceptability labels is used to train a neural network-based model which generates acceptability scores for proposed identifiers submitted to an application. The identifiers do not include word separators. The model includes a character-level embedding layer. Using a trained version of the model, an acceptability score is obtained for a proposed identifier, and one or more operations are initiated based at least in part on the obtained score.

Abstract: A computer-implemented method of training an artificial neural network (ANN) by generating one or more learned parameters for use during a subsequent inference phase of the trained ANN, comprises providing training data representing first and second input signals, the second input signal exhibiting one or more transformations relative to the first signal selected from a set of transformations; using the ANN and in response to the one or more parameters, generating a magnitude and phase representation of each of the first and second input signals; and training the one or more parameters, in dependence upon a constraint which causes the magnitude representation of the first input signal and the magnitude representation of the second input signal to tend to become more similar to one another, the training step comprising: detecting an error signal; and updating the one or more parameters in dependence upon the error signal.

Abstract: Provided is process, including: a parsing a sequence of tokens from natural-language text; computing, with a transformer encoder of a neural network, a feature vector of the sequence of tokens; inputting the feature vector into a probabilistic head of the neural network comprising a Gaussian-process layer and determining, with the probabilistic head, both a latent mean and a latent variance for each of a plurality of candidate output classes; computing, with the probabilistic head, predictive class probabilities from the latent means and latent variances; selecting, with the neural network, one of the candidate output classes based on the predictive class probabilities; and determining an uncertainty of the selection based on the latent means and the latent variances.

Abstract: A method, apparatus, and product includes obtaining a representation of a quantum circuit that is configured to manipulate a plurality of qubits over a plurality of cycles where the representation defines a first order of the plurality of qubits. A second order of the plurality of qubits is determined that is different from the first order of the plurality of qubits, wherein said determining the second order is based on an objective function that is configured to provide scores based on respective lengths of circuit components in graphical representations of the quantum circuit. A graphical representation of the quantum circuit is generated that displays the plurality of qubits in accordance with the second order, and the graphical representation is displayed.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling an agent. One of the methods includes obtaining an observation characterizing a current state of the environment and data identifying a task currently being performed by the agent; processing the observation and the data identifying the task using a high-level controller to generate a high-level probability distribution that assigns a respective probability to each of a plurality of low-level controllers; processing the observation using each of the plurality of low-level controllers to generate, for each of the plurality of low-level controllers, a respective low-level probability distribution; generating a combined probability distribution; and selecting, using the combined probability distribution, an action from the space of possible actions to be performed by the agent in response to the observation.

Abstract: A method for determining agent trajectories in a multi-agent scenario includes capturing, for each agent, previous trajectories of the agents and a vicinity of the agent in a local reference frame of the agent; and coding, for each agent, the previous trajectories of the agents, captured in the local reference frame of the agent, into trajectory feature vectors and the vicinity of the agent, captured in the local reference frame of the agent, into vicinity feature vectors using an encoder neural network. The method further includes processing, for each agent, the trajectory feature vectors, depending on one another and depending on the vicinity feature vectors, into local-context feature vectors using an attention-based neural network; and processing the local-context feature vectors for all agents into a global-context feature vector for each agent using a common attention-based neural transformation network.

Abstract: Methods and systems for improved optimization of machine learning model explanation processes are provided. In one embodiment, a method is provided that includes generating an approximation model using a model explanation process in order to present one or more relevant features for an output as generated by a model based on an input. Generating the approximation model may include determining a stability measure for a first set of parameter values for the model explanation process. The first set of parameter values to be iteratively adjusted to identify and optimize set of parameter values. The approximation model may then be generated using the model explanation process configured according to the optimize set of parameter values.

Abstract: Systems and methods are described herein for improvements to generating improved processing pathway management and determining optimal processing increments to complete the processing pathway using synthetic subsets of processing increments. For example, systems and methods are described herein for generating synthetic subsets of processing increments using models and algorithms.

Abstract: Systems and methods for identifying suspected anomalies in time series data are disclosed. An example method may receiving time series data for at least one quantity, the time series data including values of the at least one quantity at each of a plurality of times, determining a list of gradients for the time series data, each gradient in the list of gradients based on two or more values of the time series data separated by a specified number of values of the time series data, deriving a plurality of statistics based on the determined list of gradients, and performing a supervised machine learning process based on the derived plurality of statistics to generate a trained machine learning model for identifying one or more suspected anomalies in the time series data.

Abstract: Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to enhance capabilities of peer devices. In an implementation, at least one agent to: identify one or more learnable capabilities enabled by one or more parameters that are accessible via receipt of one or more message at the one or more communication devices from one or more other computing devices; and determine a utility of augmenting at least one of the one or more learning engines with at least one of the one or more learnable capabilities.

Abstract: A hardware implementation of a neural network and a method of processing data in such a hardware implementation are disclosed. Input data for a plurality of layers of the network is processed in blocks, to generate respective blocks of output data. The processing proceeds depth-wise through the plurality of layers, evaluating all layers of the plurality of layers for a given block, before proceeding to the next block.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive structural analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive structural analysis using at least one of table column classification machine learning models, table column clustering machine learning models, structural variance generation machine learning models, and emergence report generation machine learning models.

Abstract: A device and a computer-implemented method for machine learning. First input data are provided which encompass information concerning dimensions and options for the machine learning. At least one of the options is associated with at least one of the dimensions as a function of information concerning the dimensions and options for at least one test case for the machine learning. A combination of options for a subset of the dimensions that is lacking in the set of test cases is determined, and a test case is determined for this combination.

Abstract: A computer-implemented method for training a neural network, which, in particular, is configured to classify physical measuring variables. The neural network is trained with the aid of a training data set. Pairs including an input signal and an associated desired output signal are drawn from the training data set for training. An adaptation of parameters of the neural network occurs as a function of an output signal of the neural network, when the input signal is supplied, and as a function of the desired output signal. The drawing of pairs always takes place from the entire training data set.

Abstract: A plurality of tensors is obtained, and the plurality of tensors is reformatted to provide a plurality of reformatted tensors of a select dimension. The reformatting includes adding padding to at least one reformatted tensor of the plurality of reformatted tensors. The plurality of reformatted tensors is concatenated to provide a concatenated tensor. The concatenated tensor is to be used in recurrent neural network processing.

Abstract: Embodiments for learning semantic description of data based on physical knowledge in a computing environment by a processor. Physical knowledge data and semantic labels associated with data from one or more data sources may be learned. Source attributes of the one or more data sources may be associated with one or more classes and concepts of a plurality of ontologies based on the physical knowledge data and the semantic labels to generate textual descriptors of the data.

Abstract: A building system of a building including one or more memory devices configured to store one or more instructions that, when executed on one or more processors, cause the one or more processors to exercise a building entity causing building entity data to be generated associated with the building entity, the building entity data indicating a result of exercising the building entity and collect the building entity data. The instructions cause the one or more processors to identify, based on a relational model, one or more relationships between one or more building entities and the building entity, wherein the one or more relationships indicate that exercising the building entity affects operation of the one or more building entities and identify that the building is experiencing a performance issue by analyzing the building entity data and the one or more relationships.

Abstract: In an approach for policy security shifting left of infrastructure as code compliance, a processor trains a neural network model to classify a code per policy and provide a policy vector score for the code associated with one or more policies. A processor enables the neural network model to scan and score a new code during a continuous integration and continuous deployment pipeline. A processor outputs a scanned score of the new code to a user. A processor retrains the neural network model by capturing a continuous integration and continuous deployment change and run-time compliance posture that occurs as a response by the user.

Abstract: An apparatus and method for generating user-specific self-executing data structures are described. The apparatus includes at least a processor and a memory communicatively coupled to the at least a processor. The memory includes instructions configuring the at least a processor to receive a user profile comprising a plurality of user related data associated with a user, analyze the plurality of user related data, determine at least one user designation associated with the user as a function of the analyzing the plurality of user related data, and generate a self-executing record as a function of the user designation for the user.

Abstract: An example system includes a processor to learn a shared embedding space on unlabeled videos using speech visual correspondence. The processor can learn a number of additional embeddings including a question plus video embedding and an answer embedding using the shared embedding space to generate a trained visual question answering model. The processor can execute a visual question answering based on the trained visual question answering model.