Techniques herein train a multilayer perceptron, sparsify edges of a graph such as the perceptron, and store edges and vertices of the graph. Each edge has weight. A computer sparsifies perceptron edges. The computer performs a forward-backward pass on the perceptron to calculate a sparse Hessian matrix. Based on that Hessian, the computer performs quasi-Newton perceptron optimization. The computer repeats this until convergence. The computer stores edges in an array and vertices in another array. Each edge has weight and input and output indices. Each vertex has input and output indices. The computer inserts each edge into an input linked list based on its weight. Each link of the input linked list has the next input index of an edge. The computer inserts each edge into an output linked list based on its weight. Each link of the output linked list comprises the next output index of an edge.

Approaches for optimizing network demand forecasting models and network topology using hyperparameter selection are provided. An approach includes defining a pool of features that are usable in models that predict demand of network resources, wherein the pool of features includes at least one historical forecasting feature and at least one event forecasting feature. The approach also includes generating, using a computer device, an optimal model using a subset of features selected from the pool of features. The approach further includes predicting future demand on a network using the optimal model. The approach additionally includes allocating resources in the network based on the predicted future demand.

A device configured to emulate a correlithm object processing system comprises a memory and one or more processors. The memory stores a mapping table that includes multiple context value entries, multiple corresponding source value entries, and multiple corresponding target value entries. Each context value entry includes a correlithm object. The one or more processors receive at least one input source value and a context input value. The one or more processors identify a context value entry from the mapping table that matches the context input value based at least in part upon n-dimensional distances between the context input value and each of the context value entries. The one or more processors identify a portion of the source value entries corresponding to the identified context value entry, and further identifies a source value entry that matches the input source value. The one or more processors identify a target value entry corresponding to the identified source value entry.

A device configured to emulate a correlithm object processing system comprises a memory and one or more processors. The memory stores a mapping table that includes multiple context value entries, multiple corresponding source value entries, and multiple corresponding target value entries. Each context value entry includes a correlithm object. The one or more processors receive at least one input source value and a context input value. The one or more processors identify a context value entry from the mapping table that matches the context input value based at least in part upon n-dimensional distances between the context input value and each of the context value entries. The one or more processors identify a portion of the source value entries corresponding to the identified context value entry, and further identifies a source value entry that matches the input source value. The one or more processors identify a target value entry corresponding to the identified source value entry.

A correlithm object processing system that includes a trainer configured to send a node entry request to a node engine that triggers the node engine to generate an entry in a node table. The trainer is further configured to receive a source correlithm object and a target correlithm object in response to sending the node entry request. The trainer is further configured to send a real world input value and the source correlithm object to a sensor engine which triggers the sensor engine to generate an entry in a sensor table linking the real world input value and the source correlithm object. The trainer is further configured to send a real world output value and the target correlithm object to an actor engine which triggers the actor engine to generate an entry in an actor table linking the real world output value and the target correlithm object.

A correlithm object processing system that includes a trainer configured to send a node entry request to a node engine that triggers the node engine to generate an entry in a node table. The trainer is further configured to receive a source correlithm object and a target correlithm object in response to sending the node entry request. The trainer is further configured to send a real world input value and the source correlithm object to a sensor engine which triggers the sensor engine to generate an entry in a sensor table linking the real world input value and the source correlithm object. The trainer is further configured to send a real world output value and the target correlithm object to an actor engine which triggers the actor engine to generate an entry in an actor table linking the real world output value and the target correlithm object.

A system and method for training a system for monitoring administration of medication. The method includes the steps of a method for training a medication administration monitoring apparatus, comprising the steps of defining one or more predetermined medications and then acquiring information from one or more data sources of a user administering medication. A first network is trained to recognize a first step of a medication administration sequence, and then a second network is trained to recognize a second step of a medication administration sequence based upon the training of the first network.

A medium storing a program for causing a computer to execute processing including: obtaining pieces of combination data each including attribute information indicating attributes of a person and information indicating which choice is selected from choices; generating, for each choice, converted data obtained by converting the information into information indicating whether the choice is selected; identifying, for each choice, based on the converted data, an attribute, from the attributes, that has a correlation greater than a criterion with the selection of the choice and a condition; identifying a common condition among conditions of different choices based on the condition; and determining a plan for improving a selection result of the choice for a target who matches one of the different choices and who matches the common condition based on a result of an analysis between the attributes and the choice by using the converted data matching the common condition.

In accordance with an embodiment, described herein is a system and method for logistic matrix factorization of implicit feedback data, with application to media environments or streaming services. While users interact with an environment or service, for example a music streaming service, usage data reflecting implicit feedback can be collected in an observation matrix. A logistic function can be used to determine latent factors that indicate whether particular users are likely to prefer particular items. Exemplary use cases include providing personalized recommendations, such as personalized music recommendations, or generating playlists of popular artists.

Technical solutions are described to accelerate training of a multi-layer convolutional neural network. According to one aspect, a computer implemented method is described. A convolutional layer includes input maps, convolutional kernels, and output maps. The method includes a forward pass, a backward pass, and an update pass that each include convolution calculations. The described method performs the convolutional operations involved in the forward, the backward, and the update passes based on a first, a second, and a third perforation map respectively. The perforation maps are stochastically generated, and distinct from each other. The method further includes interpolating results of the selective convolution operations to obtain remaining results. The method includes iteratively repeating the forward pass, the backward pass, and the update pass until the convolutional neural network is trained. Other aspects such as a system, apparatus, and computer program product are also described.