Microsoft Technology Licensing, LLC

Apple Inc.

Lucent Technologies Inc.

Alice Zheng

John Carlton Platt

Christopher Bruce Weare

Christopher J Burges

Steven E Swenson

Steven E Swnson

Paramvir Bahl

Ping Wang

Sharad Agarwal

Trishul A Chilimbi

Danyel Aharon Fisher

Ratul Mahajan

Wim Sweldens

Madanlal S Musuvathi

Kenneth Lee Clarkson

Srikrishna Sridhar

Jack W Stokes

Mikhail Bilenko

Carlos E Guestrin

Edward Wilkins Hardy

Yucheng Low

Robert Eric Fitzgerald

Bodicherla Aditya Prakash

Shuo-Hsien Hsiao

Tim Muss

Zach Nation

Nishant A Mehta

Eric Wolfe

David M Andrzejewski

Brian Kent

Chris DuBois

The various embodiments described herein include methods, systems and/or devices used to visualize data. In one aspect, a method is performed by a computing system having one or more processors and memory. The method includes (1) receiving a request from a user to visualize data, the data stored in a graph dataflow processing system, and (2) in response to the request, invoking an interactive graphical user interface (GUI) for display to the user, the GUI including a first set of visualization data corresponding to a first subset of the data.

User-interface for developing applications that apply machine learning

An analysis management system (AMS) is described that analyzes the in-field behavior of a program resource installed on a collection of computing devices, such as mobile telephone devices or the like. In operation, the AMS can instruct different devices to collect data regarding different observation points associated with the program resource, thus spreading the reporting load among the devices. Based on the data that is collected, the AMS can update a dependency graph that describes dependencies among the observation points associated with the program resource. The AMS can then generate new directives based on the updated dependency graph. The AMS can also use the dependency graph and the collected data to infer information regarding observation points that is not directly supplied by the collected data.

Generating dependency graphs for analyzing program behavior

Technologies pertaining to tuning a hyper-parameter configuration of a learning algorithm are described. The learning algorithm learns parameters of a predictive model based upon the hyper-parameter configuration. Candidate hyper-parameter configurations are identified, and statistical hypothesis tests are undertaken over respective pairs of candidate hyper-parameter configurations to identify, for each pair of candidate hyper-parameter configurations, which of the two configurations is associated with better predictive performance. The technologies described herein take into consideration the stochastic nature of training data, validation data, and evaluation functions.

Tuning hyper-parameters of a computer-executable learning algorithm

The described implementations relate to analysis of computing programs. One implementation provides a technique that can include accessing values of input variables that are processed by test code and runtime values that are produced by the test code while processing the input variables. The technique can also include modeling relationships between the runtime values and the values of the input variables. The relationships can reflect discontinuous functions of the input variables.

Modeling software behavior using learned predicates

An analysis system is described for identifying potentially malicious activity within a computer network. It performs this task by interacting with a user to successively remove known instances of non-malicious activity, to eventually reveal potentially malicious activity. The analysis system interacts with the user by inviting the user to apply labels to identified examples of network behavior; upon response by the user, the analysis system supplies new examples of network behavior to the user. In one implementation, the analysis system generates such examples using a combination of feature-based analysis and graph-based analysis. The graph-based analysis relies on analysis of graph structure associated with access events, such as by identifying entropy scores for respective portions of the graph structure.

Analysis of computer network activity by successively removing accepted types of access events

A system and method for identifying a root cause of a wait in a computer system are provided. Given the identity of a thread of interest and time window, a longest wait period for the thread of interest within the time window is identified. The longest wait period is used as a starting node to generate a ready tree by walking backwards through the data in a system trace to construct a tree of readying events that ready threads for running on a processor. A potentially anomalous chain of events is automatically identified and highlighted in the ready tree. A visualization of the ready tree is presented to a user so that the user can explore the events in the tree and annotate the automatically generated tree to aid in problem diagnosis.

Visualization tool for system tracing infrastructure events

A system and method for generating a list is provided. The system includes a seed item input subsystem, an item identifying subsystem, a descriptive metadata similarity determining subsystem and a list generating subsystem that builds a list based, at least in part, on similarity processing performed on seed item descriptive metadata and user item descriptive metadata and user selected thresholds applied to such similarity processing. The method includes inexact matching between identifying metadata associated with new user items and identifying metadata stored in a reference metadata database. The method further includes subjecting candidate user items to similarity processing, where the degree to which the candidate user items are similar to the seed item is determined, and placing user items in a list of items based on user selected preferences for (dis)similarity between items in the list and the seed item.

Auto playlist generator

Received symbols are decoded in a multiple-antenna communication system using lattice-based decoding. The symbols are generated using a modulation constellation, e.g., a diagonal modulation constellation, and the constellation is characterized as a lattice for decoding purposes. For example, if a given communication link of the multiple-antenna communication system includes M transmitter antennas and a single receiver antenna, the diagonal modulation constellation can be characterized as a lattice in M dimensions. A differential decoding operation for received differential symbols involves a determination of the closest point in the lattice corresponding to the constellation. This determination may be made in an efficient manner using a basis reduction algorithm which generates an approximately orthogonal basis for the lattice, and then utilizes component-wise rounding to determine the closest point. The lattice-based decoding has a complexity which is polynomial rather than exponential in the particular number of antennas and the system rate, but is nonetheless able to deliver error rate performance which approximates that of maximum likelihood decoding.

Method and apparatus for fast decoding in a multiple-antenna wireless communication system

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Seattle, WA, United States of America

Alice Zheng