Sas Institute Inc.

North Carolina State University

Augmentir, Inc.

Mustafa Kabul

Lawrence Edmund Lewis

James Allen Cox

Saratendu Sethi

Jordan Riley Benson

Samuel Paul Leeman-Munk

Ravinder Devarajan

David James Caira

Christopher Graham Healey

Kalpesh Padia

Shaoliang Nie

John Thomas Canosa

Philip J Huber

Lawrence Fan

Afshin Oroojlooy Jadid

Mohammad Reza Nazari

Russell Fadel

A system and method of generating a plurality of actionable insights is disclosed herein. A computing system retrieves data corresponding to a work procedure. Each work procedure includes a plurality of steps. The computing system generates a predictive model for each actionable insight using a plurality of machine learning models by generating an input training based on the retrieved work procedure data and learning, by the plurality machine learning models, a metric corresponding to each actionable insight based on each respective input training set. The input data set for each actionable insight includes actionable insight specific information. The computing system receives a request to generate a plurality of actionable insights for a current work procedure. The computing system generates, via the predictive models, a plurality of metrics for a plurality of actionable insights based on data corresponding to the current work procedure.

System and method for improving human-centric processes

Exemplary embodiments can maximize long-term value in a machine learning system. The system may employ an offline training process and an online training process. In the offline training process, an initial policy is learned to provide a warm start to the online training process. In the online training process, the system applies concurrent reinforcement learning across multiple environments, with the goal of learning efficient policies in real time from in-flight user data in one environment, and applying the learned policies to other environments. With the combination of offline training and online training, the system is able to improve initial performance through the warm start, while adapting to a changing context through concurrent reinforcement learning.

Methods and systems for reinforcement learning

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

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.

Visualizing convolutional neural networks

Interactive visualizations of a convolutional neural network are provided. For example, a graphical user interface (GUI) can include a matrix having symbols indicating feature-map values that represent likelihoods of particular features being present or absent at various locations in an input to a convolutional neural network. Each column in the matrix can have feature-map values generated by convolving the input to the convolutional neural network with a respective filter for identifying a particular feature in the input. The GUI can detect, via an input device, an interaction indicating that that the columns in the matrix are to be combined into a particular number of groups. Based on the interaction, the columns can be clustered into the particular number of groups using a clustering method. The matrix in the GUI can then be updated to visually represent each respective group of columns as a single column of symbols within the matrix.

Interactive visualizations of a convolutional neural network

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Apex, NC, United States of America

Mustafa Kabul