Snap Inc.

Emre Yamangil

Jaewook Chung

Wisam Dakka

Christopher Yale Crutchfield

Yanping Chen

Jiwoon Jeon

Theresa Barton

Xin Chen

Aymeric Damien

Lucas Ou-Yang

Keyang Zhang

Sergey Tulyakov

Michael Brian Murray

Andre Madeira

Yantao Zheng

Sergei Kotcur

Igor Kudriashov

Andrei Damian

Ranveer Kunal

Andrew Wan

Chunhui Zhu

Diwakar Punjani

Disclosed are methods and systems for ranking content. In one aspect, a method of ranking content for display includes identifying, via hardware processing circuitry, interactions by a single account with content pairs, each of the content in the content pairs included in a plurality of content, aggregating, via the hardware processing circuitry, the identified interactions across a plurality of accounts, associating, via the hardware processing circuitry, probabilities with each content in the plurality of content based on the aggregated interactions, ranking, via the hardware processing circuitry, the plurality of content based on the associated probabilities; and selecting, via the hardware processing circuitry, content ranked above a threshold for display.

Ranking content for display

A mobile device can implement a neural network-based style transfer scheme to modify an image in a first style to a second style. The style transfer scheme can be configured to detect an object in the image, apply an effect to the image, and blend the image using color space adjustments and blending schemes to generate a realistic result image. The style transfer scheme can further be configured to efficiently execute on the constrained device by removing operational layers based on resources available on the mobile device.

Realistic neural network based image style transfer

Manipulating images using computationally expensive machine learning schemes can be implemented using server-generated models of the machine learning schemes that are transmitted to a client device for application. The schemes can include convolutional neural networks having a kernel comprising a plurality of low-rank matrices.

Fast image style transfers

Disclosed are implementations for ranking a feed in a social network. In one aspect, a method of generating a feed includes identifying a group of users associated with a first user, identify notifications for the first user based on the group of users, determine one or more actions that may result from each of the identified notifications, rank the notifications based on the one or more determined actions; and display the notifications to the first user according to the ranking.

Ranking notifications in a social network feed

A system of machine learning schemes can be configured to efficiently perform image processing tasks on a user device, such as a mobile phone. The system can selectively detect and transform individual regions within each frame of a live streaming video. The system can selectively partition and toggle image effects within the live streaming video.

Device-based image modification of depicted objects

Growing community of inventors

San Francisco, CA, United States of America

Emre Yamangil