We present a no-code Artificial Intelligence (AI) platform called Trinity with the main design goal of enabling both machine learning researchers and non-technical geospatial domain experts to experiment with domain-specific signals and datasets for solving a variety of complex problems on their own.  This versatility to solve diverse problems is achieved by transforming complex Spatio-temporal datasets to make them consumable by standard deep learning models, in this case, Convolutional Neural Networks (CNNs), and giving the ability to formulate disparate problems in a standard way, eg. semantic segmentation. With an intuitive user interface, a feature store that hosts derivatives of complex feature engineering, a deep learning kernel, and a scalable data processing mechanism, Trinity provides a powerful platform for domain experts to share the stage with scientists and engineers in solving business-critical problems. It enables quick prototyping, rapid experimentation and reduces the time to production by standardizing model building and deployment. In this paper, we present our motivation behind Trinity and its design along with showcasing sample applications to motivate the idea of lowering the bar to using AI.

Trinity: A No-Code AI platform for complex spatial datasets

Photos (on iOS, iPadOS, and macOS) is an integral way for people to browse, search, and relive life's moments with their friends and family. Photos uses a number of machine learning algorithms, running privately on-device, to help curate and organize images, Live Photos, and videos. An algorithm foundational to this goal recognizes people from their visual appearance.

Recognizing People in Photos Through Private On-Device Machine Learning

We study the problem of novel view synthesis of a scene comprised of 3D objects. We propose a simple yet effective approach that is neither continuous nor implicit, challenging recent trends on view synthesis. We demonstrate that although continuous radiance field representations have gained a lot of attention due to their expressive power, our simple approach obtains comparable or even better novel view reconstruction quality comparing with state-of-the-art baselines while increasing rendering speed by over 400x. Our model is trained in a category-agnostic manner and does not require scene-specific optimization. Therefore, it is able to generalize novel view synthesis to object categories not seen during training. In addition, we show that with our simple formulation, we can use view synthesis as a self-supervision signal for efficient learning of 3D geometry without explicit 3D supervision.

 Fast and Explicit Neural View Synthesis

This paper was accepted at the Theory and Practice of Differential Privacy workshop at the ICML 2021 conference.

Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Thakurta demonstrates that random shuffling amplifies differential privacy guarantees of locally randomized data. Such amplification implies substantially stronger privacy guarantees for systems in which data is contributed anonymously and has lead to significant interest in the shuffle model of privacy.

We show that random shuffling of $n$ data records that are input to $\varepsilon_0$-differentially private local randomizers results in an $(O((1 − e^{-\varepsilon_0})\sqrt\frac{e^{\varepsilon_0} \log (1/\delta)}{n}), \delta)$-differentially private algorithm. This significantly improves over previous work and achieves the asymptotically optimal dependence in $\varepsilon_0$. Our result is based on a new approach that is simpler than previous work and extends to approximate differential privacy with nearly the same guarantees. Our work also yields an empirical method to derive tighter bounds the resulting ε and we show that it gets to within a small constant factor of the optimal bound. As a direct corollary of our analysis, we derive a simple and asymptotically optimal algorithm for discrete distribution estimation in the shuffle model of privacy. We also observe that our result implies the first asymptotically optimal privacy analysis of noisy stochastic gradient descent that applies to sampling without replacement.

A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling

Apple machine learning teams are engaged in state of the art research in machine learning and artificial intelligence. Learn about the latest advancements.

Overview


 ## Accepted Paper

 ### Conference Accepted Paper

### [Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP](/research/evaluating-entity-disambiguation-amber)

*Anthony Chen, Pallavi Gudipati, Shayne Longpre, Xiao Ling, Sameer Singh* 

## Talks and Workshops 

“Meet Apple” is an opportunity to learn more about our machine learning teams, working at Apple, and how to apply to both full-time and internship positions. All registered ACL attendees are invited. This talk will be held virtually on August 3 at 11:00 am PDT. 

Learn more about how to join the event by visiting our [virtual booth.](https://gather.town/app/T5x469zIFF3Nkfpe/ACL-IJCNLP)

## Affinity Events

Apple is a sponsor of [Queer in AI](https://sites.google.com/view/queer-in-ai/acl-2021), who will be holding socials on Saturday, July 31 and Sunday, August 1.

Learn more about Apple’s company-wide efforts in [inclusion and diversity](https://www.apple.com/diversity/).

<div class="callout">

Attending ACL this year? Learn more about how to apply to internship and full-time positions on our Machine Learning and Computer Vision teams by visiting our [virtual booth.](https://gather.town/app/T5x469zIFF3Nkfpe/ACL-IJCNLP)


Let's innovate together. Build amazing machine-learned experiences with Apple. Discover opportunities for researchers, students, and developers by visiting our [Work With Us page.](/work-with-us)

</div>


<p>Apple is sponsoring the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021). This conference focuses on Computational Linguistics and Natural Language Processing and will take place virtually on August 1 to 6. </p>

Apple at ACL 2021



 ## Accepted Papers

 ### Conference Accepted Papers

### [Learning Neural Network Subspaces](/research/learning-neural-network-subspaces)

*Mitchell Wortsman, Maxwell Horton, Carlos Guestrin, Ali Farhadi, Mohammad Rastegari* 

### [Lossless Compression of Efficient Private Local Randomizers](/research/lossless-compression)

*Vitaly Feldman, Kunal Talwar* 

### [Private Adaptive Gradient Methods for Convex Optimization](/research/private-adaptive-gradient-methods)

*Hilal Asi, John Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar* 

### [Private Stochastic Convex Optimization: Optimal Rates in ℓ1 Geometry](/research/private-stochastic-convex-optimizations)

*Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar* 

### [Tensor Programs IIb: Architectural Universality of Neural Tangent Kernel Training Dynamics](/research/tensor-programs)

*Etai Littwin, Greg Yang* 

### [Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning](/research/offline-reinforcement-learning)

*Yue Wu, Shuangfei Zhai, Nitish Srivastava, Josh Susskind, Jian Zhang, Ruslan Salakhutdinov, Hanlin Goh* 

 ### Workshop Papers

### [Implicit Greedy Rank Learning in Autoencoders via Overparameterized Linear Networks](/research/implicit-acceleration)

*Shih-Yu Sun, Vimal Thilak, Etai Littwin, Omid Saremi, Joshua M. Susskind*

### [Implicit Acceleration and Feature Learning in Infinitely Wide Neural Networks with Bottlenecks](/research/implicit-greedy-rank)

*Etai Littwin, Omid Saremi, Shuangfei Zhai, Vimal Thilak, Hanlin Goh, Joshua M. Susskind, Greg Yang*

### [When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?](/research/memorization-of-irrelevant-training)

*Gavin R Brown, Mark Bun, Vitaly Feldman, Adam Smith, Kunal Talwar*

### [A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling](/research/privacy-amplification-by-shuffling)

*Vitaly Feldman, Audra McMillan, Kunal Talwar*

## Talks and Workshops 

Apple is hosting a panel on internships, where attendees can learn more about internship opportunities across our machine learning teams. It will be held virtually on July 21 at 2:00 pm PDT. 

“Meet Apple” is an opportunity to learn more about our machine learning teams, working at Apple, and how to apply to full-time positions. All registered ICML attendees are invited. This talk will be held virtually on July 21 at 2:30 pm PDT. 

Learn more about how to join each event by visiting our [virtual booth.](https://topia.io/apple-icml-2021)

## Affinity Events

Apple is a sponsor of [LatinX in AI](https://www.latinxinai.org/events/icml-2021), which will be holding a workshop at ICML on July 19. Guillermo Sapiro will be participating in the mentoring hour that will take place during the workshop as a mentor.

Apple is also a sponsor of [Women in Machine Learning](https://wimlworkshop.org/icml2021/), which will be holding a workshop July 21. Lizi Ottens will be speaking about how our on-device machine learning powers intelligent experiences in Apple products.

In addition to LatinX in AI and Women in Machine Learning, Apple is also a sponsor of [Queer in AI](https://sites.google.com/view/queer-in-ai/icml-2021), who will be holding a workshop on July 19, and [Black in AI](https://blackinai.github.io/#/events/social-icml-2021) who will be holding a virtual social on July 20.

Learn more about Apple’s company-wide efforts in [inclusion and diversity](https://www.apple.com/diversity/).

<div class="callout">

Attending ICML this year? Learn more about how to apply to internship and full-time positions on our Machine Learning and Computer Vision teams by visiting our [virtual booth.](https://topia.io/apple-icml-2021)


Let's innovate together. Build amazing machine-learned experiences with Apple. Discover opportunities for researchers, students, and developers by visiting our [Work With Us page.](/work-with-us)

</div>


<p>Apple is sponsoring the thirty-eighth International Conference on Machine Learning (ICML). This conference focuses on the advancement of the branch of artificial intelligence known as machine learning and will take place virtually from July 18 to 24.</p>

Machine Learning
Research at Apple.

Recent research.

Trinity: A No-Code AI platform for complex spatial datasets

Recognizing People in Photos Through Private On-Device Machine Learning

Fast and Explicit Neural View Synthesis

A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling

Updates and events.

Apple at ACL 2021

Apple at ICML 2021

Discover opportunities in Machine Learning.

Machine LearningResearch at Apple.

Recent research.

Trinity: A No-Code AI platform for complex spatial datasets

Recognizing People in Photos Through Private On-Device Machine Learning

Fast and Explicit Neural View Synthesis

A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling

Updates and events.

Apple at ACL 2021

Apple at ICML 2021

Discover opportunities in Machine Learning.

Machine Learning
Research at Apple.