Sas Institute Inc.

North Carolina State University

Lawrence Edmund Lewis

Steven E Krueger

Joshua David Griffin

Xindian Long

Mohammadreza Nazari

Amirhassan Fallah Dizche

Jorge Manuel Gomes Da Silva

James Allen Cox

Yan Xu

Ralph Walter Abbey

Saratendu Sethi

Jordan Riley Benson

Samuel Paul Leeman-Munk

Ravinder Devarajan

Scott Russell Pope

David James Caira

Xingqi Du

Christopher Graham Healey

Kalpesh Padia

Liping Cai

Shaoliang Nie

Mustafa Kabul

A system, method, and computer-program product includes implementing a cross-process queue within a single computer that is configured to transfer a data block between an operating system process executing a write operation and an operating system process executing a read operation, initializing in-memory cell indices within the cross-process queue that include a write operation index tracking index values of one or more cells within the cross-process queue that are available to write and a read operation index tracking index values of one or more cells within the cross-process queue that are available to read, and implementing a cell synchronization data structure tracking states of a plurality of cells of the index of cells of the cross-process queue.

Systems and methods for implementing and using a cross-process queue within a single computer

A system, method, and computer-program product includes commencing a parent computer process based on receiving a request to perform an analytical operation on one or more datasets, commencing at least one child computer process that is launched by the parent computer process when the parent computer process initiates an execution of the analytical operation on the one or more datasets, transmitting, by the at least one child computer process, a request to the parent computer process to retrieve the one or more datasets, writing, by the parent computer process, the one or more datasets to a cross-process queue based on the parent computer process receiving the requests, reading, by the at least one child computer process, the one or more datasets from the cross-process queue, and executing, using an analytical application executing on the least one child computer process, the analytical operation based on the one or more datasets.

Systems and methods for executing an analytical operation across a plurality of computer processes

A system, method, and computer-program product includes computing, by a controller node, a hyperparameter search space for a plurality of hyperparameters; selecting, by the controller node, a plurality of hyperparameter search points from the hyperparameter search space; instructing, by the controller node, one or more worker nodes to concurrently train a plurality of machine learning models based on the plurality of hyperparameter search points; receiving, from the one or more worker nodes, a plurality of performance metrics that measure a performance of the plurality of machine learning models; determining, by the controller node, one or more sets of optimal hyperparameter values based on the plurality of performance metrics; and outputting, by the controller node, the one or more sets of optimal hyperparameter values.

Systems and methods for parallel exploration of a hyperparameter search space

A system, method, and computer-program product includes receiving, by a worker process, a plurality of chunks of data from a client process; deriving, by the worker process, an input pattern for feeding the plurality of chunks of data to a machine learning model; caching, by the worker process, a subset of data elements of the plurality of chunks of data specified by the input pattern based on a data caching policy; and training the machine learning model by feeding the subset of data elements cached by the worker process and a remainder of data elements in the plurality of chunks of data when requested by the input pattern.

Systems and methods for multi-language training of machine learning models

A system, method, and computer-program product includes receiving, by a controller node, a request to execute a client process associated with a first programming language and a plurality of threads; launching, by the controller node, a plurality of multi-language worker processes based on a number of threads associated with the client process; and instructing, by the controller node, the plurality of multi-language worker processes to execute the plurality of threads associated with the client process.

A system, method, and computer-program product includes selecting, by a controller node, a plurality of hyperparameter search points from a hyperparameter search space; instructing, by the controller node, one or more worker nodes to concurrently train a plurality of machine learning models for a target number of epochs using the plurality of hyperparameter search points; receiving, from the one or more worker nodes, a plurality of performance metrics that measure a performance of the plurality of machine learning models during the target number of epochs; and removing, by the controller node, one or more underperforming hyperparameter search points from the plurality of hyperparameter search points according to a pre-defined performance metric ranking criterion associated with the plurality of performance metrics.

A computing system provides distributed training of a neural network model. Explore phase options, exploit phase options, a subset of a training dataset, and a validation dataset are distributed to a plurality of computing devices. (a) Execution of the model by the computing devices is requested using the subset stored at each computing device. (b) A first result of the execution is received from a computing device. (c) Next configuration data for the neural network model is selected based on the first result and distributed to the computing device. (a) to (c) is repeated until an exploration phase is complete. (d) Execution of the neural network model is requested. (e) A second result is received. (f) Next configuration data is computed based on the second result and distributed to the computing device. (d) to (f) is repeated until an exploitation phase is complete. The next configuration data defines the model.

Two-phase distributed neural network training system

Recurrent neural networks (RNNs) can be visualized. For example, a processor can receive vectors indicating values of nodes in a gate of a RNN. The values can result from processing data at the gate during a sequence of time steps. The processor can group the nodes into clusters by applying a clustering method to the values of the nodes. The processor can generate a first graphical element visually indicating how the respective values of the nodes in a cluster changed during the sequence of time steps. The processor can also determine a reference value based on multiple values for multiple nodes in the cluster, and generate a second graphical element visually representing how the respective values of the nodes in the cluster each relate to the reference value. The processor can cause a display to output a graphical user interface having the first graphical element and the second graphical element.

Interactive visualizations for a recurrent neural network

A method of determining a false and/or a true positive rate is provided. A true count value and a false count value are initialized for probability bins. For a plurality of records, a truth of event occurrence and a probability of occurrence are read; a probability bin that includes the probability of occurrence is determined; the true count value of the determined probability bin is incremented when the truth of event occurrence indicates true; and the false count value of the determined probability bin is incremented when the truth of event occurrence indicates false. A true positive rate and a false positive rate are computed for each probability bin based on the true count value, the false count value, a determined total number of true event occurrences, and a determined total number of false event occurrences.

Computation of receiver operating characteristic curves

Convolutional neural networks can be visualized. For example, a graphical user interface (GUI) can include a matrix of symbols indicating feature-map values that represent a likelihood of a particular feature being present or absent in an input to a convolutional neural network. The GUI can also include a node-link diagram representing a feed forward neural network that forms part of the convolutional neural network. The node-link diagram can include a first row of symbols representing an input layer to the feed forward neural network, a second row of symbols representing a hidden layer of the feed forward neural network, and a third row of symbols representing an output layer of the feed forward neural network. Lines between the rows of symbols can represent connections between nodes in the input layer, the hidden layer, and the output layer of the feed forward neural network.

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Durham, NC, United States of America

Lawrence Edmund Lewis