Abstract: Techniques regarding qubit coupling structures that enable RIP gates are provided. For example, one or more embodiments described herein can comprise an apparatus that can include a coupling structure coupled to a first qubit and a second qubit. The coupling structure can have a plurality of coupling pathways. A coupling pathway from the plurality of coupling pathways can be a resonator. Also, the first qubit can be coupled to a first end of the resonator, and the second qubit can be coupled to a point along a length of the resonator.

Abstract: Disclosed examples include after a first tuning of hyperparameters in a hyperparameter space, selecting first hyperparameter values for respective ones of the hyperparameters; generating a polygonal shaped failure region in the hyperparameter space based on the first hyperparameter values; setting the first hyperparameter values to failure before a second tuning of the hyperparameters; and selecting second hyperparameter values for the respective ones of the hyperparameters in a second tuning region after the second tuning of the hyperparameters in the second tuning region, the second tuning region separate from the polygonal shaped failure region.

Abstract: A method for analyzing a simulation of the execution of a quantum circuit comprises: a step of post-selecting (S2) one or more particular values of one or more qubits at one or more steps of the simulation, a step of retrieving (S5), by an iterator (7), all or some of the quantum states of the quantum state vector(s) derived from the post-selection(s) of qubits, a step of analyzing (S6) the part of the simulation that corresponds to the post-selection(s) of qubits and to the quantum state vector(s) retrieved.

Abstract: Techniques are described for performing active surveillance and learning for machine learning (ML) model authoring and deployment workflows. In an embodiment, a method comprises applying, by a system comprising a processor, a primary ML model trained on a training dataset to data samples excluded from the training dataset to generate inferences based on the data samples. The method further comprises employing, by the system, one or more active surveillance techniques to regulate performance of the primary ML model in association with the applying, wherein the one or more active surveillance techniques comprise at least one of, performing a model scope evaluation of the primary ML model relative to the data samples or using a domain adapted version of the primary ML model to generate the inferences.

Abstract: An information processing device according to the present invention includes: a memory; and at least one processor coupled to the memory. The processor performs operations. The operations includes: extracting a feature of a period or a frequency in a plurality of pieces of time-series data acquired by measuring an object; classifying the pieces of time-series data into a group related to the feature; generating, for each of the groups, a model that represents a relationship among the pieces of time-series data classified into the group; and selecting the model in which strength of the relationship satisfies a predetermined condition.

Abstract: Examples herein propose operating redundant ML models which have been trained using a boosting technique that considers hardware faults. The embodiments herein describe performing an evaluation process where the performance of a first ML model is measured in the presence of a hardware fault. The errors introduced by the hardware fault can then be used to train a second ML model. In one embodiment, a second evaluation process is performed where the combined performance of both the first and second trained ML models is measured in the presence of a hardware fault. The resulting errors can then be used when training a third ML model. In this manner, the three trained ML models are trained to be error aware. As a result, during operation, if a hardware fault occurs, the three ML models have better performance relative to three ML models that where not trained to be error aware.

Abstract: Techniques for implementing unsupervised anomaly detection by self-prediction are provided. In one set of embodiments, a computer system can receive an unlabeled training data set comprising a plurality of unlabeled data instances, where each unlabeled data instance includes values for a plurality of features. The computer system can further train, for each feature in the plurality of features, a supervised machine learning (ML) model using a labeled training data set derived from the unlabeled training data set, receive a query data instance, and generate a self-prediction vector using at least a portion of the trained supervised ML models and the query data instance, where the self-prediction vector indicates what the query data instance should look like if it were normal. The computer system can then generate an anomaly score for the query data instance based on the self-prediction vector and the query data instance.

Abstract: A bearing fault diagnosis method based on a fuzzy broad learning mode includes steps of constructing an initial fuzzy broad learning model based on a broad learning system and a fuzzy system, training the initial fuzzy broad learning model through training set data to obtain a target fuzzy broad learning model. The training set data includes a plurality of bearing vibration signal data with a fault type label; and a membership value of a bearing vibration signal data to be tested is calculated by the target fuzzy broad learning model. A fault type of the bearing to be tested is determined based on the membership value. The bearing fault diagnosis method reduces learning time. When determining the fault type of the bearing to be tested by the target fuzzy broad learning model, it has strong robustness, fast diagnosis speed and high fault diagnosis accuracy.

Abstract: Embodiments of the present invention are directed to facilitating event forecasting. In accordance with aspects of the present disclosure, a set of events determined from raw machine data is obtained. The events are analyzed to identify leading indicators that indicate a future occurrence of a target event, wherein the leading indicators occur during a search period of time the precedes a warning period of time, thereby providing time for an action to be performed prior to an occurrence of a predicted target event. At least one of the leading indicators is used to predict a target event. An event notification is provided indicating the prediction of the target event.

Abstract: Techniques for classifier generalization in a supervised learning process using input encoding are provided. In one aspect, a method for classification generalization includes: encoding original input features from at least one input sample {right arrow over (x)}S with a uniquely decodable code using an encoder E(?) to produce encoded input features E({right arrow over (x)}S), wherein the at least one input sample {right arrow over (x)}S comprises uncoded input features; feeding the uncoded input features and the encoded input features E({right arrow over (x)}S) to a base model to build an encoded model; and learning a classification function {tilde over (C)}E(?) using the encoded model, wherein the classification function {tilde over (C)}E(?) learned using the encoded model is more general than that learned using the uncoded input features alone.

Abstract: An artificial intelligence (AI) parameter configuration method for a racing AI model performed by an AI parameter configuration device is provided. A first parameter set including m sets of AI parameters is obtained. Each of the m sets of AI parameters is used by the racing AI model to travel on a track. The racing AI model is controlled to undergo an adaptation degree test according to each of the m sets of AI parameters to obtain m adaptation degrees. The m adaptation degrees are positively correlated with travel distances of the racing AI model on the track according to the m sets of AI parameters. A second parameter set is generated according to the first parameter set in a case that all the m adaptation degrees are less than an adaptation degree threshold. In a case that a target adaptation degree in the m adaptation degrees is greater than the adaptation degree threshold, an AI parameter corresponding to the target adaptation degree is configured as a target AI parameter.

Abstract: There is provided a computing apparatus that includes: a retaining unit configured to retain an approximation table that approximately represents an activation function of a neural network, the approximation table mapping between a plurality of discrete input samples of the activation function and output samples respectively corresponding to the plurality of input samples; and a computing unit configured to convert an input value of activation function computation to an output value using the approximation table retained by the retaining unit when the activation function is selected for the activation function computation. The plurality of input samples of the approximation table are set such that input samples more distant from a reference point in the domain of the activation function have a larger neighboring sample interval.

Abstract: Embodiments include methods performed by a processor of a vehicle for allocating processing resources to concurrently-executing neural networks. The methods may include determining a priority of each of a plurality of neural networks executing on a vehicle processing system based on a contribution of each neural network to overall vehicle safety performance, and allocating computing resources to the plurality of neural networks based on the determined priority of each neural network. In some embodiments, the methods may dynamically adjust hyperparameters of one or more neural networks.

Abstract: A computing device identifies an anomaly among a plurality of observation vectors. An observation vector is projected using a predefined orthogonal complement matrix. The predefined orthogonal complement matrix is determined from a decomposition of a low-rank matrix. The low-rank matrix is computed using a robust principal component analysis algorithm. The projected observation vector is multiplied by a predefined demixing matrix to define a demixed observation vector. The predefined demixing matrix is computed using an independent component analysis algorithm and the predefined orthogonal complement matrix. A detection statistic value is computed from the defined, demixed observation vector. When the computed detection statistic value is greater than or equal to a predefined anomaly threshold value, an indicator is output that the observation vector is an anomaly.

Abstract: Embodiments disclosed herein generally relate to a method and system for generating a container image. A computing system receives a request from a remote computer to provision a container comprising a machine learning model. The computing system generates a first API accessible by the remote computer. The computing system receives one or more parameters for the container via the API. The one or more parameters include a machine learning model type. The computing system retrieves from a library of a plurality of machine learning models a machine learning model corresponding to a type of model specified in the one or more parameters. The computing system generates a container image that includes the machine learning model. The computing system provisions a container based on the container image.

Abstract: Systems and methods for automatic recognition of entities related to cloud incidents are described. A method, implemented by at least one processor, for processing cloud incidents related information, including entity names and entity values associated with incidents having a potential to adversely impact products or services offered by a cloud service provider is provided. The method may include using at least one processor, processing the cloud incidents related information to convert at least words and symbols corresponding to a cloud incident into machine learning formatted data. The method may further include using a machine learning pipeline, processing at least a subset of the machine learning formatted data to recognize entity names and entity values associated with the cloud incident.

Abstract: A method, a computer program product, and a system of adversarial semi-supervised one-shot training using a data stream. The method includes receiving a data stream based on an observation, wherein the data stream includes unlabeled data and labeled data. The method also includes training a prediction model with the labeled data using stochastic gradient descent based on a classification loss and an adversarial term and training a representation model with the labeled data and the unlabeled data based on a reconstruction loss and the adversarial term. The adversarial term is a cross-entropy between the middle layer output data from the models. The classification loss is a cross-entropy between the labeled data and an output from the prediction model. The method further includes updating a discriminator with middle layer output data from the prediction model and the representation model and based on a discrimination loss, and discarding the data stream.

Abstract: Disclosed are a method and apparatus for energy-aware deep neural network compression. A network pruning method for deep neural network compression includes measuring importance scores of a network unit by using an energy-based criterion with respect to a deep learning model, and performing network pruning of the deep learning model based on the importance scores.

Abstract: Methods, systems, and apparatus for selecting actions to be performed by an agent interacting with an environment. One system includes a high-level controller neural network, low-level controller network, and subsystem. The high-level controller neural network receives an input observation and processes the input observation to generate a high-level output defining a control signal for the low-level controller. The low-level controller neural network receives a designated component of an input observation and processes the designated component and an input control signal to generate a low-level output that defines an action to be performed by the agent in response to the input observation.

Abstract: One or more computing devices, systems, and/or methods for generating time-preserving embeddings are provided. User trails of user activities performed by users are generated. Frequencies at which the activities were performed are identified. Indices are assigned to a set of activities identified from the activities as having frequencies above a threshold. Activity descriptions of the set of activities are mapped to the indices to generate a vocabulary. A model is trained using the user trails, timestamps of the activities, and the vocabulary to learn a set of time-preserving embeddings.