Harvard College

University of Toronto

Microsoft Technology Licensing, LLC

Université De Sherbrooke

Richard Zemel

Kevin Jordan Swersky

Ryan P Adams

Roland Jasper Snoek

Steven Mark Drucker

Gang Hua

Paul A Viola

Michael David Revow

Hugo Larochelle

Kevin Swersky

Techniques for use in connection with performing optimization using an objective function that maps elements in a first domain to values in a range. The techniques include using at least one computer hardware processor to perform: identifying a first point at which to evaluate the objective function at least in part by using an acquisition utility function and a probabilistic model of the objective function, wherein the probabilistic model depends on a non-linear one-to-one mapping of elements in the first domain to elements in a second domain; evaluating the objective function at the identified first point to obtain a corresponding first value of the objective function; and updating the probabilistic model of the objective function using the first value to obtain an updated probabilistic model of the objective function.

Systems and methods for Bayesian optimization using non-linear mapping of input

A system for organizing images includes an extraction component that extracts visual information (e.g., faces, scenes, etc.) from the images. The extracted visual information is provided to a comparison component which computes similarity confidence data between the extracted visual information. The similarity confidence data is an indication of the likelihood that items of extracted visual information are similar. The comparison component then generates a visual distribution of the extracted visual information based upon the similarity confidence data. The visual distribution can include groupings of the extracted visual information based on computed similarity confidence data. For example, the visual distribution can be a two-dimensional layout of faces organized based on the computed similarity confidence data&#x2014;with faces in closer proximity faces computed to have a greater probability of representing the same person. The visual distribution can then be utilized by a user to sort, organize and/or tag images.

Image organization based on image content

Growing community of inventors

Toronto, Canada

Richard Zemel