Abstract: Techniques are disclosed for identifying multimodal subevents within an event having spatially-related and temporally-related features. In one example, a system receives a Spatio-Temporal Graph (STG) comprising (1) a plurality of nodes, each node having a feature descriptor that describes a feature present in the event, (2) a plurality of spatial edges, each spatial edge describing a spatial relationship between two of the plurality of nodes, and (3) a plurality of temporal edges, each temporal edge describing a temporal relationship between two of the plurality of nodes. Furthermore, the STG comprises at least one of: (1) variable-length descriptors for the feature descriptors or (2) temporal edges that span multiple time steps for the event. A machine learning system processes the STG to identify the multimodal subevents for the event. In some examples, the machine learning system comprises stacked Spatio-Temporal Graph Convolutional Networks (STGCNs), each comprising a plurality of STGCN layers.

Abstract: A method includes generating a training data set comprising a plurality of training examples, wherein each training example is generated by receiving map data associated with a road portion, receiving sensor data associated with a road agent located on the road portion, defining one or more corridors associated with the road portion based on the map data and the sensor data, extracting a plurality of agent features associated with the road agent based on the sensor data, extracting a plurality of corridor features associated with each of the one or more corridors based on the sensor data, and for each corridor, labeling the training example based on the position of the road agent with respect to the corridor, and training a neural network using the training data set.

Abstract: A method and a system are for providing a medical data structure for a patient. The system includes a plurality of data sources, each data source to provide medical data of the patient; a computing device to implement an artificial neural network structure a plurality of encoding modules, each being realized as an artificial neural network configured and trained to generate, from the medical data from the corresponding data source, a corresponding encoded output matrix; a weighting gate module for each of the encoding modules; a concatenation module configured to concatenate weighted output matrices of the weighting gates to a concatenated output matrix; and an aggregation module realized as an artificial neural network configured and trained to receive the concatenated output matrix and to generate therefrom the medical data structure for the patient, the artificial neural network structure being trained as a whole using a cost function.

Abstract: A feature map caching method of a convolutional neural network includes a connection analyzing step and a plurality of layer operation steps. The connection analyzing step is for analyzing a network to establish a convolutional neural network connection list. The convolutional neural network connection list includes a plurality of tensors and a plurality of layer operation coefficients. Each of the layer operation coefficients includes a step index, at least one input operand label and an output operand label. The step index as a processing order for the layer operation step. At least one of the layer operation steps is for flushing at least one of the tensors in a cache according to a distance between the at least one of the layer operation steps and a future layer operation step of the layer operation steps. The distance is calculated according to the convolutional neural network connection list.

Abstract: A log circuit for piecewise linear approximation is disclosed. The log circuit identifies an input associated with a logarithm operation to be performed using piecewise linear approximation. The log circuit then identifies a range that the input falls within from various ranges associated with piecewise linear approximation (PLA) equations for the logarithm operation, where the identified range corresponds to one of the PLA equations. The log circuit computes a result of the corresponding PLA equation based on the respective operands of the equation. The log circuit then returns an output associated with the logarithm operation, which is based at least partially on the result of the PLA equation.

Abstract: A computing device selects new test configurations for testing software. Software under test is executed with first test configurations to generate a test result for each test configuration. Each test configuration includes a value for each test parameter where each test parameter is an input to the software under test. A predictive model is trained using each test configuration of the first test configurations in association with the test result generated for each test configuration based on an objective function value. The predictive model is executed with second test configurations to predict the test result for each test configuration of the second test configurations. Test configurations are selected from the second test configurations based on the predicted test results to define third test configurations. The software under test is executed with the defined third test configurations to generate the test result for each test configuration of the third test configurations.

Abstract: A neural network mapping method and a neural network mapping apparatus are provided. The method includes: mapping a calculation task for a preset feature map of each network layer in a plurality of network layers in a convolutional neural network to at least one processing element of a chip; acquiring the number of phases needed by a plurality of processing elements in the chip for completing the calculation tasks, and performing a first stage of balancing on the number of phases of the plurality of processing elements; and based on the number of the phases of the plurality of processing elements obtained after the first stage of balancing, mapping the calculation task for the preset feature map of each network layer in the plurality of network layers in the convolutional neural network to at least one processing element of the chip subjected to the first stage of balancing.

Abstract: A cumulative reward of a target system type is predicted by training a prediction model by performing an iteration for each time step. The iteration includes recursively updating a matrix by using the weighted difference between an eligibility trace of a current time step and an eligibility trace of a previous time step, recursively updating a vector by using a reward of a subsequent time step and the eligibility trace of the current time step, and recursively updating an eligibility trace of a subsequent time step by using a feature vector of the subsequent time step. Each feature vector is an encoded representation of a state of a training system of the target system type at a corresponding time step. The matrix and the vector are output as the prediction model for estimating the cumulative reward of a target time step of a target system of the target system type.

Abstract: A method, system and computer readable medium for generating a cognitive insight comprising: receiving training data, the training data being based upon interactions between a user and a cognitive learning and inference system; performing a ranked insight machine learning operation on the training data; generating a cognitive profile based upon the information generated by performing the ranked insight machine learning operations; and, generating a cognitive insight based upon the cognitive profile generated using the plurality of machine learning operations.

Abstract: A method, system, and computer program product for configuring a computer for data similarity determination using Bregman divergence may include storing a data set having plural data pairs with one or more data points corresponding to one or more features and generating a trained input convex neural network (ICNN) using the data set, the ICNN having one or more parameters. Training the ICNN may include extracting one or more features for each piece of data in the first data pair, generating an empirical Bregman divergence for the first data pair, and computing one or more gradients between the one or more features within the first data pair using known target distances and the computed empirical Bregman divergence.

Abstract: A device for operating a machine learning system. The machine learning system is assigned a predefinable rollout, which characterizes a sequence in which each of the layers ascertains an intermediate variable. When assigning the rollout, each connection or each layer is assigned a control variable, which characterizes whether the intermediate variable of each of the subsequent connected layers is ascertained according to the sequence or regardless of the sequence. A calculation of an output variable of the machine learning system as a function of an input variable of the machine learning system is controlled as a function of the predefinable rollout. Also described is a method for operating the machine learning system.

Abstract: A method may include receiving, at a recommendation engine, a first indication to create a first sourcing event that includes a first object and a second object. The recommendation engine may respond to the first indication by updating a learning model to increment a first frequency of the first object being included in a sourcing event, a second frequency of the second object being included in a sourcing event, and/or a third frequency of the first object and the second object being included simultaneously in a sourcing event. The recommendation engine may receive a second indication to create a second sourcing event. In response to the second indication, the recommendation engine may apply the learning model to generate a recommendation to add, to the second sourcing event, the first object instead of the second object. Related systems and articles of manufacture are also provided.

Abstract: A machine learning computing system for extracting structured data objects from electronic documents comprising unstructured text includes a first data repository storing a plurality of electronic documents including at least one text data object and an expert system computing device. The expert system computing device includes a processor and a non-transitory memory device storing instructions causing the expert system to receive a first data object comprising unstructured data identified from an electronic document stored in the first data repository, process, a first set of rules to identify at least one key-value pair data object from the first data object; process, by an inference engine module, a second set of rules to identify at least one free text data object from the first data object and store, in a non-transitory memory device, the at least one key-value pair and the at least one free text data object.

Abstract: A learning device includes a structure search unit that searches for a first learned model structure obtained by selecting search space information in accordance with a target constraint condition of target hardware for each of a plurality of convolution processing blocks included in a base model structure in a neural network model; a parameter search unit that searches for a learning parameter of the neural network model in accordance with the target constraint condition; and a pruning unit that deletes a unit of at least one of the plurality of convolution processing blocks in the first learned model structure based on the target constraint condition and generates a second learned model structure.

Abstract: The systems and methods may use one or more artificial intelligence models that predict an effect of a predicted event on a current state of the system. For example, the model may predict how a rate of change in time-series data may be altered throughout the first time period based on the predicted event.

Abstract: A training apparatus includes an acquiring unit that acquires a first model including an input layer to which input information is input; a plurality of intermediate layers that executes a calculation based on a feature of the input information that has been input; and an output layer that outputs output information that corresponds to output of the intermediate layer. The training apparatus includes a training unit that trains the first model such that, when predetermined input information is input to the first model, the first model outputs predetermined output information that corresponds to the predetermined input information and intermediate information output from a predetermined intermediate layer among the intermediate layers becomes close to feature information that corresponds to a feature of correspondence information that corresponds to the predetermined input information.

Abstract: Devices and methods related to spiking neural units in memory. One device includes a memory array and a complementary metal-oxide semiconductor (CMOS) coupled to the memory array and located under the memory array, wherein the CMOS includes a spiking neural unit comprising logic configured to receive an input to increase a weight stored in a memory cell of the memory array, collect the weight from the memory cell of the memory array, accumulate the weight with an increase based on the input, compare the accumulated weight to a threshold weight, and provide an output in response to the accumulated weight being greater than the threshold weight.

Abstract: An example operation may include one or more of generating, by a plurality of training participant clients, gradient calculations for machine learning model training, each of the training participant clients comprising a training dataset, converting, by a training aggregator coupled to the plurality of training participant clients, the gradient calculations to a plurality of transaction proposals, receiving, by one or more endorser nodes or peers of a blockchain network, the plurality of transaction proposals, executing, by each of the endorser nodes or peers, a verify gradient smart contract, and providing endorsements corresponding to the plurality of transaction proposals to the training aggregator.

Abstract: A method for receiving an ownership graph, wherein the ownership graph comprises a first set of nodes and a first set of directional edges, and wherein each of the first set of directional edges connects two nodes and indicates ownership of a first node by a second node, each node having at most one owner, the ownership graph being acyclic. The method further includes receiving a dependency graph that also comprises a set of nodes and a set of directional edges. The method further includes creating a respective enumerating variable declaration for each node in a path from an owner node to a root node in the ownership graph. The method further includes creating a respective accessing variable declaration for each owner node in the dependency graph of the current node.

Abstract: A neural network implementation is disclosed. The implementation allows the computations for the neural network to be performed on either an accelerator or a processor. The accelerator and the processor share a memory and communicate over a bus to perform the computations and to share data. The implementation uses weight compression and pruning, as well as parallel processing, to reduce computing, storage, and power requirements.