公开(公告)号：US20170371856A1

公开(公告)日：2017-12-28

申请号：US15630462

申请日：2017-06-22

Applicant: SAS Institute Inc.

Inventor： Ethem F. Can ,  Richard W. Crowell ,  James Tetterton ,  Jared Peterson ,  SARATENDU SETHI

IPC: G06F17/24 ,  G06F17/30 ,  G06F17/22 ,  G06K9/68 ,  G06K9/46

CPC classification number: G06F17/248 ,  G06F16/5846 ,  G06F17/2211 ,  G06F17/2881 ,  G06K9/00449 ,  G06K9/3233 ,  G06K9/4642 ,  G06K9/4676 ,  G06K9/469 ,  G06K9/6201 ,  G06K9/6267 ,  G06K9/6892 ,  G06N3/02

Abstract: Various embodiments are generally directed to systems for summarizing data visualizations (i.e., images of data visualizations), such as a graph image, for instance. Some embodiments are particularly directed to a personalized graph summarizer that analyzes a data visualization, or image, to detect pre-defined patterns within the data visualization, and produces a textual summary of the data visualization based on the pre-defined patterns detected within the data visualization. In various embodiments, the personalized graph summarizer may include features to adapt to the preferences of a user for generating an automated, personalized computer-generated narrative. For instance, additional pre-defined patterns may be created for detection and/or the textual summary may be tailored based on user preferences. In some such instances, one or more of the user preferences may be automatically determined by the personalized graph summarizer without requiring the user to explicitly indicate them. Embodiments may integrate machine learning and computer vision concepts.

公开(公告)号：US20180095632A1

公开(公告)日：2018-04-05

申请号：US15724029

申请日：2017-10-03

Applicant: SAS Institute Inc. ,  North Carolina State University

Inventor： SAMUEL PAUL LEEMAN-MUNK ,  SARATENDU SETHI ,  CHRISTOPHER GRAHAM HEALEY ,  SHAOLIANG NIE ,  KALPESH PADIA ,  RAVINDER DEVARAJAN ,  DAVID JAMES CAIRA ,  JORDAN RILEY BENSON ,  JAMES ALLEN COX ,  LAWRENCE E. LEWIS ,  MUSTAFA ONUR KABUL

IPC: G06F3/0481 ,  G06N3/04 ,  G06T11/60 ,  G06F9/44

CPC classification number: G06F3/04812 ,  G06F8/34 ,  G06F9/451 ,  G06F2203/04803 ,  G06N3/04 ,  G06N3/105 ,  G06T11/60

Abstract: 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.

公开(公告)号：US20180096241A1

公开(公告)日：2018-04-05

申请号：US15584984

申请日：2017-05-02

Applicant: SAS Institute Inc. ,  North Carolina State University

Inventor： CHRISTOPHER GRAHAM HEALEY ,  SHAOLIANG NIE ,  KALPESH PADIA ,  RAVINDER DEVARAJAN ,  DAVID JAMES CAIRA ,  JORDAN RILEY BENSON ,  SARATENDU SETHI ,  JAMES ALLEN COX ,  LAWRENCE E. LEWIS ,  SAMUEL PAUL LEEMAN-MUNK

IPC: G06N3/04

CPC classification number: G06N3/04 ,  G06N3/105

Abstract: Deep neural networks can be visualized. For example, first values for a first layer of nodes in a neural network, second values for a second layer of nodes in the neural network, and/or third values for connections between the first layer of nodes and the second layer of nodes can be received. A quilt graph can be output that includes (i) a first set of symbols having visual characteristics representative of the first values and representing the first layer of nodes along a first axis; (ii) a second set of symbols having visual characteristics representative of the second values and representing the second layer of nodes along a second axis; and/or (iii) a matrix of blocks between the first axis and the second axis having visual characteristics representative of the third values and representing the connections between the first layer of nodes and the second layer of nodes.

公开(公告)号：US20190156153A1

公开(公告)日：2019-05-23

申请号：US15952833

申请日：2018-04-13

Applicant: SAS Institute Inc.

Inventor： AYSU EZEN CAN ,  NING JIN ,  ETHEM F. CAN ,  XIANGQIAN HU ,  SARATENDU SETHI

IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06F17/27 ,  G06K9/46

Abstract: Machine-learning models and behavior can be visualized. For example, a machine-learning model can be taught using a teaching dataset. A test input can then be provided to the machine-learning model to determine a baseline confidence-score of the machine-learning model. Next, weights for elements in the teaching dataset can be determined. An analysis dataset can be generated that includes a subset of the elements that have corresponding weights above a predefined threshold. For each overlapping element in both the analysis dataset and the test input, (i) a modified version of the test input can be generated that excludes the overlapping element, and (ii) the modified version of the test input can be provided to the machine-learning model to determine an effect of the overlapping element on the baseline confidence-score. A graphical user interface can be generated that visually depicts the test input and various elements' effects on the baseline confidence-score.

公开(公告)号：US20190034558A1

公开(公告)日：2019-01-31

申请号：US16138446

申请日：2018-09-21

Applicant: SAS Institute Inc. ,  North Carolina State University

Inventor： SAMUEL PAUL LEEMAN-MUNK ,  SARATENDU SETHI ,  CHRISTOPHER GRAHAM HEALEY ,  SHAOLIANG NIE ,  KALPESH PADIA ,  RAVINDER DEVARAJAN ,  DAVID JAMES CAIRA ,  JORDAN RILEY BENSON ,  JAMES ALLEN COX ,  LAWRENCE E. LEWIS

IPC: G06F17/30 ,  G06F3/0484 ,  G06F3/0481 ,  G06K9/62 ,  G06N3/04

Abstract: 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.