ICML 2022

Apple sponsored the International Conference on Machine Learning (ICML) which was held in Baltimore, Maryland from July 17 to 23. ICML is a conference dedicated to the advancement of machine learning.

All ICML attendees were invited to stop by the Apple booth (booth number 1111, located in Hall B of the Baltimore Convention Center) to check out our demos and chat with available recruiters and booth staff.

Accepted Papers

Conference Accepted Papers

Efficient Representation Learning via Adaptive Context Pooling

Chen Huang, Walter Talbott, Navdeep Jaitly, Josh Susskind

This paper was accepted to the main session at ICML as a spotlight paper.

Optimal Algorithms for Mean Estimation under Local Differential Privacy

Hilal Asi, Vitaly Feldman, Kunal Talwar

This paper was accepted to the main session at ICML as an oral paper.

Position Prediction as an Effective Pre-training Strategy

Shuangfei Zhai, Navdeep Jaitly, Jason Ramapuram, Dan Busbridge, Tatiana Likhomanenko, Joseph Y Cheng, Walter Talbott, Chen Huang, Hanlin Goh, Joshua M Susskind

This paper was accepted to the main session at ICML as a spotlight paper.

Private Frequency Estimation via Projective Geometry

Vitaly Feldman, Jelani Nelson, Huy Nguyen, Kunal Talwar

This paper was accepted to the main session at ICML as an spotlight paper.

Self-Conditioning Pre-Trained Language Models

Xavier Suau, Luca Zappella, Nicholas Apostoloff

This paper was accepted to the main session at ICML as a spotlight paper.

Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models

Jen-Hao Rick Chang, Ashish Shrivastava, Hema Swetha Koppula, Xiaoshuai Zhang, Oncel Tuzel

This paper was accepted to the main session at ICML as a spotlight paper.

All ICML attendees are invited to stop by the Apple booth (booth number 1111, located in Hall B of the Baltimore Convention Center) to experience these demos in person.

Demos

RoomPlan

RoomPlan technology allows the user to captures a room and its defining objects in a parametric format within minutes. Capture progress is automatically displayed and easy to understand at a glance. The resulting room capture is provided as a parametric representation of the room and can optionally be exported to USD, USDA or USDZ formats. The technology is supported on any of the Apple devices (iPad and iPhone) that are equipped with LiDAR sensor.

LiveText

This demo aims to illustrate one of the user facing Visual Intelligence capabilities in our operating systems. A distinguishing factor of the workflows are that they are deeply integrated into the operating system, and run efficiently on device. In this demo, we will interact with visual content via text in static images and streaming camera through LiveText.

Live Text was introduced in iOS 15 through static image and camera based workflows.

ARKitScenes

Scene understanding is an active research area. Commercial depth sensors, such as Kinect, have enabled the release of several RGB-D datasets over the past few years which spawned novel methods in 3D scene understanding. More recently with the launch of the LiDAR sensor in Apple's iPads and iPhones, high quality RGB-D data is accessible to millions of people on a device they commonly use. This opens a whole new era in scene understanding for the Computer Vision community as well as app developers. The fundamental research in scene understanding together with the advances in machine learning can now impact people's everyday experiences. However, transforming these scene understanding methods to real-world experiences requires additional innovation and development. In this paper we introduce ARKitScenes. It is not only the first RGB-D dataset that is captured with a now widely available depth sensor, but to our best knowledge, it also is the largest indoor scene understanding data released. In addition to the raw and processed data from the mobile device, ARKitScenes includes high resolution depth maps captured using a stationary laser scanner, as well as manually labeled 3D oriented bounding boxes for a large taxonomy of furniture. We further analyze the usefulness of the data for two downstream tasks: 3D object detection and color-guided depth upsampling. We demonstrate that our dataset can help push the boundaries of existing state-of-the-art methods and it introduces new challenges that better represent real-world scenarios.

Acknowledgements

Samy Bengio, Marco Cuturi and Ronan Collobert are senior area chairs for ICML 2022.

Navdeep Jaitly is an area chair for ICML 2022.

Luca Zappella, Shuangfei Zhai, Walter Talbott, Barry Theobald, Tatiana Likhomanenko, Preetum Nakkiran, Xavier Suau and Chen Huang are reviewers for ICML 2022.