Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating prediction outputs characterizing a set of entities. In one aspect, a method comprises: obtaining data defining a graph, comprising: (i) a set of nodes, wherein each node represents a respective entity from the set of entities, (ii) a current set of edges, wherein each edge connects a pair of nodes, and (iii) a respective current embedding of each node; at each of a plurality of time steps: updating the respective current embedding of each node, comprising processing data defining the graph using a graph neural network; and updating the current set of edges based at least in part on the updated embeddings of the nodes; and at one or more of the plurality of time steps: generating a prediction output characterizing the set of entities based on the current embeddings of the nodes.

Abstract: A computer system displays paths based on processing of at least one series of input data including a list of time-stamped tasks. The tasks include an identifier of an object, an identifier of an action and a piece of time information. The system includes a piece of connected user computer equipment executing a display application and at least one remote server executing an application for calculating a path model from tables.

Abstract: A method includes using a first procedure of discretizing a user specified nano-device structure for at least one quantum method with open boundary conditions. Additionally, the first procedure includes solving the at least one quantum method with open boundary conditions, thereby having a solution of the at least one quantum method with open boundary conditions. Moreover, the first procedure includes extracting a parameter out of the solution of the at least one quantum method with open boundary conditions. Furthermore, the first procedure includes discretizing at least one quantum method with closed boundary conditions to the user-specified nano-device structure using the parameter. Next, the first procedure includes solving the at least one quantum method with closed boundary conditions to the user-specified nano-device structure using the parameter. Further, the first procedure includes extracting the device behavior of the user-specified nano-device structure.

Abstract: A method and system may operate a neural network (NN), e.g. during inference or training, by executing a first tensor column comprising task instruction code representing at least one computation spanning a number of layers of the NN, the execution producing an output, and compressing that output. In order to execute a next tensor column, the output may be uncompressed to produce uncompressed output; and the second tensor column may be executed, the second tensor column including task instruction code representing at least one computation spanning a number of layers of the NN. The second tensor column may take as input the uncompressed output.

Abstract: Example aspects of the present disclosure provide systems and methods to learn machine-learned model parameters for models of quantum computing systems. In particular, example aspects of the present disclosure are directed to systems and methods to learn a deep neural network configured to predict parameter values for a physical model that models quantum dynamics of interactions between one or more qubits of a quantum gate and one or more two-level-system (TLS) defects during operation of the quantum gate through use of an evolutionary algorithm.

Abstract: A data stream is received. Data elements of the data stream are analyzed using one or more machine learning models and one or more machine learning prediction explanation implementations. Different candidate presentations are tested. The different candidate presentations are associated with machine learning results provided to different reviewers in a group of human-in-the-loop reviewers that review predictions of the one or more machine learning models. The different candidate presentations include different explanations generated by the one or more machine learning prediction explanation implementations and at least one control candidate presentation corresponding to an absent explanation. Different aspects of the testing are monitored. Results of the monitoring are used to make a selection among the different candidate presentations.

Abstract: One or more computing devices, systems, and/or methods for implementing an automated model update pipeline are provided. User behavior data associated with content provided to users may be collected. An automatic model training is invoked to train a new model to output a set of model parameters based upon a configuration specifying a target audience, features extracted from user behavior data, and training model parameters. In response to determining that the new model will outperform a deployed model on a content serving platform, an automatic model updater is invoked to update the content serving platform with the new model and a ranking profile of the new model for serving content requests.

Abstract: Conventionally, applying analytics on dataset is the scarcity of labelled data. With increase of data there is cost fact effecting nature of servicing required for data (e.g., cost in terms of resource and time and effort is high for data annotation). Though data is analysed, it may be prone to error. Present disclosure provides systems/methods for reducing volume of data to be annotated for time series data thereby reducing time and effort of resources, thus resulting in effective utilization of system's resources (e.g., memory, processor, etc.). More specifically, the method of the present disclosure adaptively modifies the volume of the data to be annotated based on the performance of the unsupervised learning method applied in the system. Moreover, in the absence of an annotation mechanism for clusters of time series data, meta data associated with the time series data is utilized for annotation and validation of dataset.

Abstract: A system and a method of training a Neural network (NN) model may include, receiving a pretrained NN model, that may include a plurality of layers, each associated with an activation matrix; selecting at least one, and performing an iterative training process on the layer. The iterative training process may include, applying an activation threshold to the activation matrix of the layer; measuring an accuracy value of the NN model; retraining the layer, while using a bimodal regularization function of one or more activation matrices of the NN model; and repeating the applying, measuring and retraining, while each repetition uses different activation threshold values. This repetition may be repeated until a maximal value of the activation threshold, where the NN model still converges, is found.

Abstract: A control engine is trained to operate a robotic camera according to a variety of different cinematographic techniques. The control engine may reconfigure the robotic camera to respond to a set of cues, to enforce a set of constraints, or to apply one or more characteristic styles. A training engine trains a network within the control engine based on training data that exemplifies cue responses, enforced constraints, and characteristic styles.

Abstract: Systems, computer-implemented methods, and/or computer program products to facilitate updating, such as averaging and/or training, of one or more statistical sets are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a computing component that updates a first statistical set with an additional statistical set from an additional system. The additional statistical set can have been generated from a parent statistical set that is based on underlying data. To update the first statistical set, the additional statistical set can be obtained by the system without obtaining the parent statistical set and without obtaining the underlying data. According to an embodiment, the first statistical set can be a model parameter set generated from a first parent statistical set that is an analytical model.

Abstract: Embodiments described herein provide safe policy improvement (SPI) in a batch reinforcement learning framework for a task-oriented dialogue. Specifically, a batch reinforcement learning framework for dialogue policy learning is provided, which improves the performance of the dialogue and learns to shape a reward that reasons the invention behind human response rather than just imitating the human demonstration.

Abstract: Performing automatic qubit relocation is disclosed herein. A processor device of a first quantum computing device receives a system stress indicator from a system monitor that tracks a status of the first quantum computing device and/or a status of qubits maintained by the first quantum computing device. A relocation rule is applied to the system stress indicator to determine whether one or more qubits located at the first quantum computing device are to be relocated. If so, the one or more qubits are relocated from the first quantum computing device to a second quantum computing device (e.g., by physically transporting the qubits via a quantum channel, or by teleporting the qubits using pairs of entangled qubits, as non-limiting examples). The processor device also updates qubit registry records for the one or more qubits to indicate that the one or more qubits have been relocated.

Abstract: A system including a multi-layer analog neural network that has a single layer of physical analog neurons that is re-usable for implementing a plurality of layers of the multi-layer analog neural network. Each of the physical analog neurons is configured to receive a neuron input and to process the neuron input to generate a neuron output that is fed as input to all physical analog neurons of the single layer, and each of the physical analog neurons includes a respective weight memory. The system controller is operable to obtain, for each physical analog neuron, a respective set of neuron weight vectors with each neuron weight vector corresponding to a respective layer of the plurality of layers of the multi-layer analog neural network; store, for each physical analog neuron, the respective set of neuron weights in the respective weight memory of the physical analog neuron.

Abstract: Systems and methods for quantum convolutional neural networks are described. Systems and methods can apply convolving and pooling layers to input qudits. The qudits can be measured to identify information about the input qudits. Systems and methods can also apply quantum convolutional neural network encoding and decoding techniques for quantum error correction.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform certain acts. The acts can include obtaining training data. The acts also can include training candidate recommendation models and an adversarial exposure model using the training data. The acts additionally can include generating recommendations based on a selected recommendation model of the candidate recommendation models. Other embodiments are described.

Abstract: Quantum process duplication is disclosed herein. In one embodiment, a processor device receives a request to duplicate a first quantum process that is associated with a first one or more qubits. The processor device obtains metadata associated with the first quantum process and its qubits, wherein the metadata includes an identifier of the first quantum process and an identifier of each of the first one or more qubits. The processor device then duplicates the first quantum process as a second quantum process. Finally, the quantum process manager associates a second one or more qubits with the second quantum process (e.g., by associating the first one or more qubits with the second quantum process as the second one or more qubits, or by allocating a new set of one or more qubits for the second quantum process as the second one or more qubits, as non-limiting examples).

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for analyzing samples. The method includes acquiring a set of feature representations associated with a set of samples. The set of samples illustratively have classification information for indicating classifications of the set of samples. The method further includes adjusting the set of feature representations so that distances between feature representations of samples corresponding to the same classification are less than a first distance threshold. The method further includes training a classification model based on the adjusted set of feature representations and the classification information. The classification model is illustratively configured to receive an input sample and determine a classification of the input sample. In this manner, a relatively accurate classification model can be trained using a small number of samples, thereby reducing computation time and required computation capacity.

Abstract: Machine learning techniques which allow machine learning to be performed even when a cost function is not a convex function are provided. A machine learning system includes a plurality of node portions which learn mapping that uses one common primal variable by machine learning based on their respective input data while sending and receiving information to and from each other. The machine learning is performed so as to minimize, instead of a cost function of a non-convex function originally corresponding to the machine learning, a proxy convex function serving as an upper bound on the cost function. The proxy convex function is represented by a formula of a first-order gradient of the cost function with respect to the primal variable or by a formula of a first-order gradient and a formula of a second-order gradient of the cost function with respect to the primal variable.

Abstract: The disclosed computer-implemented method for updating a machine-learning-based prediction model with preserved privacy may include receiving a plurality of training data sets for training a machine-learning-based global prediction model for predicting future incidents of a computing event. Each data set may include incidents of the computing event. The method may include creating, by training the global prediction model using the plurality of training data sets, an intermediate prediction model of the global prediction model. The intermediate prediction model may be a precursor state to a fully trained global prediction model. The method may further include providing the intermediate prediction model to a computing node to enable the computing node to fully train a local prediction model using both the intermediate prediction model and a local training data set. Various other methods, systems, and computer-readable media are also disclosed.