Abstract: Convolutional operators for triangle meshes are determined to construct one or more neural networks. In at least one embodiment, convolutional operators, pooling operators, and unpooling operators are determined to construct the one or more neural networks, in which the same learned weights from the one or more neural networks can further be used for triangle meshes with different topologies.

Abstract: Disclosed herein are related to a device for performing neuromorphic computing. In one aspect, a device includes a back end of line layer including a three-dimensional memory array. The three-dimensional memory array may include a plurality of memory cells to store a plurality sets of weight values of a neural network model. In one aspect, the device includes a front end of line layer including a controller. The controller may apply one or more input voltages corresponding to an input to the neural network model to the three-dimensional memory array, and receive one or more output voltages from the three-dimensional memory array to perform computations of the neural network model.

Abstract: Various embodiments analyze the application of satellite imaging and deep learning in predicting ignition and spread of major wildfires. The training data comes from NASA satellite products and historical records of wildfires in the United States. A state-of-the-art technique in neural network image classification may be utilized and yield impressive results for wildfire ignition prediction. In one embodiment, the model may achieve an accuracy rate of 93.5%, a precision rate of 93.2%, a recall rate of 88.9%, and an F-1 score of 90.8%. Direct applications of the model may include wildfire monitoring and wildfire prevention.

Abstract: The acceptance means 81 accepts input of a series of observed events. The deletion means 82 deletes, in a decision graph for determining a boolean value of the observed event based on a knowledge base in which the observed event is a node and transition relationship of each of the observed events is an edge, the edge indicating a transition to a negation side of the observed event from among the edges connected to the node corresponding to the accepted observed event. The determination means 83 determines whether or not a path from a root node to a true-valued leaf node exists in the decision graph after the deletion of the edge. The decision means 84 decides that the series of observed events is inconsistent with respect to the knowledge base when being determined that there is no path to the leaf node indicating the true value.

Abstract: A system and method for predicting next pitch are disclosed herein. A computing system retrieves pitch-by-pitch information for a plurality of events and game context information associated with each pitch in the pitch-by-pitch information. The computing system converts the pitch-by-pitch information and the game context information into a plurality of graph-based representation. A graph neural network learns to generate a pitch type prediction for each pitch based on the plurality of graph-based representations. The computing system generates a trained graph neural network based on the learning. The computing system receives a current graph-based representation of current pitch-by-pitch information for a current pitcher and current game context information. The computing system predicts, via the trained graph neural network, a pitch type for the next pitch to be delivered from the current pitcher.

Abstract: A computer-implemented method is provided. The method may comprise: obtaining at least one document to be classified; classifying, using a machine learning model including an artificial neural network (ANN) and an attention mechanism, the at least one document into at least two classes; determining, for each of the at least one document, a confidence value of the classifying, based on one or more outputs of one or more nodes comprised in the ANN; assigning, to each of the at least one document, based at least in part on the confidence value, one of at least two categories that are associated with different degrees of credibility of the classifying; and providing for display one or more of the at least one document with: the assigned category and attention information that indicates significance of one or more parts of each document provided for display in the classifying of said document.

Abstract: Systems and methods for generating a hypernetwork configured to be trained for a plurality of tasks; receiving a task preference vector identifying a hierarchical priority for the plurality of tasks, and a resource constraint as a tuple; finding tree sub-structures and the corresponding modulation of features for every tuple within an N-stream anchor network; optimizing a branching regularized loss function to train an edge hypernet; and training a weight hypernet, keeping the anchor net and the edge hypernet fixed.

Abstract: Systems and methods for retrieving similar multivariate time series segments are provided. The systems and methods include extracting a long feature vector and a short feature vector from a time series segment, converting the long feature vector into a long binary code, and converting the short feature vector into a short binary code. The systems and methods further include obtaining a subset of long binary codes from a binary dictionary storing dictionary long codes based on the short binary codes, and calculating similarity measure for each pair of the long feature vector with each dictionary long code. The systems and methods further include identifying a predetermined number of dictionary long codes having the similarity measures indicting a closest relationship between the long binary codes and dictionary long codes, and retrieving a predetermined number of time series segments associated with the predetermined number of dictionary long codes.

Abstract: One or more computer processors create a fully convolution network (FCN) comprising a plurality of 1×1 convolutions. The one or more computer processors append linear mapping layer (LM) to created FCN. The one or more computer processors capture a plurality of features utilizing multi-scale dilated convolutional kernels from the linear mapped FCN (LM-FCN). The one or more computer processors apply an average pool layer to the captured plurality of features along a temporal axis of a dilated convolutional kernel within the LM-FCN. The one or more computer processors predict a classification for subsequent time-series data utilizing the pooled plurality of features.

Abstract: A computer implemented method includes identifying, by one or more processors, a decision tree corresponding to an artificial intelligence model, detecting, by one or more processors, new data associated with an update to the identified decision tree, identifying, by one or more processors, counterfactual data corresponding to the new data, identifying, by one or more processors, one or more expected outcomes corresponding to the counterfactual data and the new data, and generating, by one or more processors, an updated decision tree based on the identified new data and the identified counterfactual data. A computer program product and computer system corresponding to the method are also disclosed.

Abstract: One example method includes deploying, in a production environment, a machine learning model that was trained using metadata created by an intermediate classical computing layer, and the metadata comprises information about one or more aspects of a quantum circuit, generating, with the machine learning model, a prediction as to how one or more computing infrastructures may be expected to perform when executing the quantum circuit, based on the prediction, making an orchestration decision concerning the quantum circuit, and orchestrating the quantum circuit to one of the computing infrastructures.

Abstract: This disclosure provides an accurate and personalized recommendation method based on a knowledge graph, which includes following steps: acquiring relevant knowledge of objects from a knowledge base according to historical behaviors of a user, and constructing a knowledge graph; initializing a vector representation of each node and its connection, and determining a receptive field of the node; generating training samples according to the historical behaviors of the user, and initializing a vector representation of all users and objects; acquiring a receptive field of an entity in the knowledge graph corresponding to the object in the training sample, then inputting the receptive field and the training sample to a graph neural network model to obtain predicted values of a possibility of an interaction between the user and the object.

Abstract: Systems and techniques that facilitate quantum-enhanced features for classical machine learning are provided. In various embodiments, a system can comprise a receiver component that can access a classical dataset. In various aspects, the system can further comprise a feature component that can generate one or more machine learning input features based on a quantum transformation of the classical data set. In various instances, the system can further comprise an execution component that can execute a classical machine learning model on the one or more machine learning input features.

Abstract: A computer implemented method obtains neural network-based model base model weight matrices for each of multiple neural network layers. First low-rank factorization matrices are added to corresponding base model weight matrices to form a first domain model. The low-rank factorization matrices are treated as trainable parameters. The first domain model is trained with first domain specific training data without modifying base model weight matrices.

Abstract: Methods, systems and media for computer vision using 2D convolution of 4D video data tensors are described. 3D convolution operations performed on 5D input tensors are simulated by performing 2D convolution of 4D tensors instead. A convolution block of a CNN performs two parallel operations: a spatial processing branch performs spatial feature extraction on a 4D tensor using 2D convolution, whereas a temporal processing branch performs temporal feature extraction on a different 4D tensor using 2D convolution. The output tensors of the spatial processing branch and the temporal processing branch are combined to generate an output tensor of the convolution block. The convolution block may include additional operations such as reshaping and/or further convolution operations to generate identically-sized output tensors for each branch, thereby eliminating the need for post-processing of the branches' output tensors prior to combining them.

Abstract: A computer-implemented method, a computer program product, and a computer system for recommending model contributions based on federated learning lineage. The computer system retrieves information of model checkpoints. The computer system trains data analytic models for monitoring activities of training rounds in a federated learning system, based on the information of the model checkpoints. The computer system sends to a user summary statistics of the model checkpoints. The computer system receives from the user natural language instructions of modifying a federated learning plan for future training rounds in the federated learning system. The computer system translates the natural language instructions into updates for the federated learning system. The computer system forwards the updates to the federated learning system.

Abstract: An apparatus includes a global memory and a systolic array. The global memory is configured to store and provide an input feature map (IFM) vector stream from an IFM tensor and a kernel vector stream from a kernel tensor. The systolic array is configured to receive the IFM vector stream and the kernel vector stream from the global memory. The systolic array is on-chip together with the global memory. The systolic array includes a plurality of processing elements (PEs) each having a plurality of vector units, each of the plurality of vector units being configured to perform a dot-product operation on at least one IFM vector of the IFM vector stream and at least one kernel vector of the kernel vector stream per unit clock cycle to generate a plurality of output feature maps (OFMs).

Abstract: An apparatus, method and computer program is described comprising: determining an initial performance of a first model, wherein determining the initial performance comprises deploying the first model at a first device; determining one or more operations for modifying the first model based on at least the initial performance of the first model and one or more user requirements; modifying the first model by performing the one or more operations; determining whether a performance of the modified first model satisfies the one or more user requirements, wherein the determining comprises deploying the modified first model at the first device; and in the event that the modified first model does not satisfy the one or more user requirements, further modifying the first model by performing one or more further operations until the performance of the modified first model satisfies the one or more user requirements, wherein the determining further one or more operations based on at least the performance of the modified f

Abstract: In one aspect, a computerized method of an artificial neural network model for implementing a distribution of automated interactions with network effects and n-tiered incentives via an interactive network comprising: training one or more artificial neural network models to automatically: establish a set of specified system-wide data center parameters, wherein the artificial neural network model is trained on a set of existing data sets and is used to automatically determine the specified system-wide data center parameters, receive an item added by a user added to the interactive network, wherein for each item added by the user adds to the system, the user assigns a specific goal, provide a set of data for each specific goals, generate a specified goal algorithm for each goal described by the user, automatically establish a distribution with a plurality of network effects and n-tiered incentives for each item the user adds, and for each transaction related to the item that occurs in the interactive network, ap

Abstract: The present disclosure relates to an apparatus and a method for computing a neural network, a board card, and a readable storage medium. The computing apparatus of the present disclosure is included in an integrated circuit apparatus. The integrated circuit apparatus includes a general interconnection interface and other processing apparatus. The computing apparatus interacts with other processing apparatus to jointly complete a computing operation specified by a user. The integrated circuit apparatus further includes a storage apparatus. The storage apparatus is connected to the computing apparatus and other processing apparatus, respectively. The storage apparatus is used for data storage of the computing apparatus and other processing apparatus.