Apple Inc.

Srikrishna Sridhar

Shawn R Scully

Zachary A Nation

Shiwen Zhao

Stephen Olivier Lemay

Gaurav Kapoor

Timothy S Paek

Brandon J Newendorp

Alexander Barraclough Brown

Michael R Siracusa

Charles Maalouf

Minwoo Jeong

Heriberto Nieto

Joanna Peterson

Christopher M Hanson

Brandt M Westing

Michael Reese Bastian

Vrushali Mundhe

Roman Holenstein

Elizabeth Ottens

Carlos E Guestrin

Gustav M Larsson

Thorsten Gernoth

Onur Canturk Hamsici

Shreyas V Joshi

Leon A Gatys

Kory R Watson

Aaron B Franklin

Karla P Vega

Ping Wang

Chen Chen

Alice Zheng

Abdelrahman Abdelhamed

Yucheng Low

Kanit Wongsuphasawat

Naga Rama Abhishek Pratapa

Torsten Becker

Marina Gray

Shayne R Longpre

Jiangkun Liu

Tim Muss

Zach Nation

Eric Wolfe

Brian Kent

Hoyt A Koepke

Chris DuBois

A device implementing a system for providing predicted RGB images includes at least one processor configured to obtain an infrared image of a subject, and to obtain a reference RGB image of the subject. The at least one processor is further configured to provide the infrared image and the reference RGB image to a machine learning model, the machine learning model having been trained to output predicted RGB images of subjects based on infrared images and reference RGB images of the subjects. The at least one processor is further configured to provide a predicted RGB image of the subject based on output by the machine learning model.

Machine learning assisted image prediction

Systems and processes for operating an intelligent automated assistant are provided. An example process includes detecting input representing motion of an electronic device and sampling an audio input with a microphone of the electronic device. The example process further includes determining, based on the audio input and the input representing motion of the electronic device, whether to initiate a virtual assistant session. In accordance with a determination to initiate the virtual assistant session, the example process includes initiating the virtual assistant session. In accordance with a determination not to initiate the virtual assistant session, the example process includes forgoing initiating the virtual assistant session.

Raise to speak

Electronic devices, methods, and non-transitory program storage devices for leveraging machine learning to perform improved image fusion and/or noise reduction are disclosed. When a capture request is received, a neural network may be used to perform fusion and denoising operations on a first set of captured input images. According to some embodiments, the neural network's architecture comprises: a first plurality of network layers configured to compute optical flow information between the first set of input images; a second plurality of network layers configured to perform, at least in part, the fusion and denoising operations on the first set of input images; and a third plurality of skip connections between layers of the second plurality of network layers, wherein parameters for each skip connection of the third plurality of skip connections are warped based on at least part of the optical flow information computed by the first plurality of network layers.

Burst image fusion and denoising using end-to-end deep neural networks

The subject technology receives, in an application on an electronic device, a message, the message being associated with a user and including information in a header portion of the message. The subject technology determines, on the electronic device, a current state of messaging activity of the user based at least in part on a log of previous events associated with the user, where the log of previous events includes information that has been hashed using a cryptographic hash function. The subject technology determines, on the electronic device using a set of rules provided by a machine learning model, that the user is likely to view the message based on the current state of the messaging activity of the user. The subject technology sets, on the electronic device, an indication that the message is important based on the determining.

Classification of messages using learned rules

The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.

Integration of learning models into a software development system

The subject technology receives information associated with a machine learning model. The subject technology determines a set of metrics based at least in part on the information associated with the machine learning model, where the set of metrics corresponds to respective indicators of performance of the machine learning model based on input data from a data set, the set of metrics further including a number of errors produced by the machine learning model when applied to the input data from the data set. Further, the subject technology displays a user interface based at least in part on the set of metrics, where the user interface includes a set of graphical elements, and the set of graphical elements further includes representations of the set of metrics, and representations of the input data from the data set utilized by the machine learning model.

Data visualization machine learning model performance

The subject technology provides for generating a set of nodes representing a tree structure, each node comprising a feature index, a flag field indicating branch directions, an execution index storing locations related to the branch directions, and a feature value for comparing with the value stored in the input feature vector. The subject technology generates evaluation data, the evaluation data comprising a first array containing index values, and a second array containing evaluation values respectively corresponding to the index values, the evaluation data representing values of leaf nodes from the set of nodes. Further, the subject technology stores the set of nodes and the evaluation data as a contiguous block of data, where the set of nodes includes a first node and a second node, the second node corresponding to a likely execution path from the first node being physically stored adjacent to the first node.

Providing a compact representation of tree structures

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

The subject technology provides for generating machine learning (ML) model code from a ML document file, the ML document file being in a first data format, the ML document file being converted to code in an object oriented programming language different than the first data format. The subject technology further provides for receiving additional code that calls a function provided by the ML model code. The subject technology compiles the ML model code and the additional code, the compiled ML model code including object code corresponding to the compiled ML model code and the compiled additional code including object code corresponding to the additional code. The subject technology generates a package including the compiled ML model code and the compiled additional code. Further, the subject technology sends the package to a runtime environment on a target device for execution.

The subject technology provides for determining that a machine learning model in a first format includes sufficient data to conform to a particular model specification in a second format, the second format corresponding to an object oriented programming language. The subject technology transforms the machine learning model into a transformed machine learning model that is compatible with the particular model specification. The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in an integrated development environment (IDE), the IDE enabling modifying of the generated code interface and the code.

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Seattle, WA, United States of America

Srikrishna Sridhar