Apple sponsored the Neural Information Processing Systems Conference (NeurIPS), which was held in New Orleans, Louisiana from November 28 to December 9. NeurIPS is a global conference focused on fostering the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects.
Below was the schedule of Apple sponsored workshops and events at NeurIPS 2022.
Monday November 28
- Generative Understanding of 3D Scenes
- 3:00 - 4:00 PM CST in Theater A
- Miguel Angel Bautista Martin
- See the full schedule of talks for Expo Talk day
- Black in AI
- 10:00 AM - 6:00 PM CST in Room 288 - 289
- LatinX in AI
- 8:00 AM - 6:00 PM CST in Room 298 - 299
- Samy Bengio will be participating in the mentoring hour as a mentor.
- Queer in AI
- 9:00 AM - 6:00 PM CST in Room 283
- Women in Machine Learning
- 7:30 AM - 6:00 PM CST in Hall I-2
- Lauren Araujo and Sefani Tadesse will be hosting a roundtable on Internships at Apple.
Tuesday November 29
- Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation
- From 4:30 - 6:00 PM CST in Hall J #707
- Jin Xu, Xiaojiang Liu, Jianhao Yan, Deng Cai, Huayang Li, Jian Li
- Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures
- 11:00 AM - 1:00 PM CST, Hall J #406
- Low-Rank Optimal Transport: Approximation, Statistics and Debiasing
- 11:00 AM - 1:00 PM CST in Hall J #507
- Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss
- From 11:00 AM - 1:00 PM CST in Hall J #331
- Subspace Recovery from Heterogeneous Data with Non-isotropic Noise
- 11:00 AM - 1:00 PM CST, Hall J #531
Wednesday November 30
- FLAIR: Federated Learning Annotated Image Repository
- 4:00 - 6:00 PM CST, Hall J #1028
Thursday December 1
- GAUDI: A Neural Architect for Immersive 3D Scene Generation
- 11:00 AM - 1:00 PM CST in Hall J #427
- MBW: Multi-view Bootstrapping in the Wild
- 11:00 AM - 1:00 PM CST in Hall J #1020
- Mean Estimation with User-level Privacy
- 4:00 - 6:00 PM CST in Hall J #730
- Supervised Training of Conditional Monge Maps
- 4:00 - 6:00 PM CST, Hall J #1035
Friday December 2
- Learning from Time Series for Health
- 9:00 AM - 5:10 PM CST, Room 392
- Accepted Paper: MAEEG: Masked Autoencoder for EEG Representation Learning
- Accepted Paper: Modeling Heart Rate Dynamics and Wearable Workout Data
- Sana Tonekaboni, one of our Apple Scholars in AI/ML, co-organized this workshop.
- Causal Machine Learning for Real-World Impact
- 9:00 AM - 5:45 PM CST, Room 296
- Accepted Paper: Causal Analysis of a Large Scale Behavioral Intervention Nudge
- Has it Trained Yet? A Workshop for Algorithmic Efficiency in Practical Neural Network Training
- 8:30 AM - 5:00 PM CST, Theater B
- Accepted Paper: The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenon
- Human in the Loop Learning
- 4:30 - 7:00 PM CST, Room 396
- Accepted Paper: Symbol Guided Hindsight Priors for Reward Learning from Human Preferences
- Accepted Paper: Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning
Saturday December 3
- Transfer Learning for Natural Language Processing
- 8:50 AM - 5:00 PM CST, Theatre A
- Accepted Paper: Impact of Language Characteristics on Multi-Lingual Text-to-Text Transfer
- Reinforcement Learning for Real Life
- 8:25 AM - 5:00 PM CST, Theatre A
- Accepted Paper: CAGEd Wisdom: Applying Reinforcement Learning to Autonomous Cyber Defense
- Self-Supervised Learning: Theory and Practice
- Workshop time TBC, Room 291 - 292
- Accepted Paper: Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer
- Accepted Paper: Contrastive Self-Supervised Learning for Skeleton Representations
- Accepted Paper: Homomorphic Self-Supervised Learning with Equivariant Networks
- I Can't Believe It's Not Better: Understanding Deep Learning Through Empirical Falsification
- 6:15 AM - 3:00 PM CST, Room 286
- Accepted Paper: Continuous Soft Pseudo-Labeling in ASR
- Arno Blaas co-organized this workshop, and Luca Zappella is a member of the coordination team.
- Machine Learning and the Physical Sciences
- 7:50 AM - 5:00 PM CST, Room 275 - 277
- Accepted Paper: Robust Hybrid Learning with Expert Augmentations
Monday December 5
- Women in Machine Learning
- 9:30 AM - 4:30 PM CST, Held Virtually
- Katherine Metcalf will give a talk on machine learning research at Apple during the workshop.
Conference Accepted Papers
FLAIR: Federated Learning Annotated Image Repository
Congzheng Song, Filip Granqvist, Kunal Talwar
GAUDI: A Neural Architect for Immersive 3D Scene Generation
Miguel Angel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander Toshev, Zhuoyuan Chen, Laurent Dinh, Shuangfei Zhai, Hanlin Goh, Daniel Ulbricht, Afshin Dehghan, Joshua Susskind
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation
Jin Xu, Xiaojiang Liu, Jianhao Yan, Deng Cai, Huayang Li, Jian Li
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures
Emmanuel Abbe, Samy Bengio, Elisabetta Cornacchia, Jon Kleinberg, Aryo Lotfi, Maithra Raghu, Chiyuan Zhang
Low-rank Optimal Transport: Approximation, Statistics and Debiasing
Meyer Scetbon, Marco Cuturi
MBW: Multi-view Bootstrapping in the Wild
Mosam Dabhi, Chaoyang Wang, Tim Clifford, László Jeni, Ian Fasel, Simon Lucey
Mean Estimation with User-level Privacy under Data Heterogeneity
Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss
Jason Altschuler, Kunal Talwar
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise
John Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar
Supervised Training of Conditional Monge Maps
Charlotte Bunne, Andreas Krause, Marco Cuturi
Workshop Accepted Papers
Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies
Melody Wolk, Andy Applebaum, Camron Dennler, Patrick Dwyer, Marina Moskowitz, Harold Nguyen, Nicole Nichols, Nicole Park, Paul Rachwalski, Frank Rau, and Adrian Webster
Large-Scale Observational Study of the Causal Effects of a Behavioral Health Nudge
Achille Nazaret, Guillermo Sapiro
Modeling Heart Rate Response to Exercise with Wearable Data
Achille Nazaret, Sana Tonekaboni, Greg Darnell, Shirley Ren, Guillermo Sapiro, Andrew C. Miller
How Soft Labels Cause Divergence in Speech Recognition for Continuous Pseudo-Labeling
Tatiana Likhomanenko, Ronan Collobert, Navdeep Jaitly, Samy Bengio
Improving Generalization with Physical Equations
Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Joern-Henrik Jacobsen
Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning
Katherine Metcalf, Miguel Sarabia and Barry-John Theobald
Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer
Andrius Ovsianas, Jason Ramapuram, Dan Busbridge, Eeshan Gunesh Dhekane, Russ Webb
MAEEG: Masked Autoencoder for EEG Representation Learning
Sherry Chien, Hanlin Goh, Chris Sandino, Joseph Yitan Cheng
Homomorphic Self-Supervised Learning
Thomas Anderson Keller, Xavier Suau, Luca Zappella
Improving Generalization with Physical Equations
Antoine Wehenkel, Jens Behrmann, Guillermo Sapiro, Joern Jacobsen, Gilles Louppe, Hsiang (Shawn) Hsu
The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenon
Vimal Thilak, Etai Littwin, Shuangfei Zhai, Omid Saremi, Roni Paiss, Josh Susskind
RoomPlan technology allows the user to captures a room and it's 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.
This demo aims to illustrate the rich information extraction and understanding capabilities embedded in select Apple products that use our state of the art search and intelligence technologies to cater to user’s information needs. These features leverage deep text and image understanding capabilities coupled with powerful search technologies to surface accurate and relevant information for user’s queries.
All NeurIPS attendees are invited to stop by the Apple booth (booth number 603, located in Hall G of the Ernest N. Morial Convention Center) to experience these demos in person.
Audra McMillan, Chong Wang, Congzheng Song, Devon Hjelm, Felix Bai, Hilal Asi, Jacob Yao, Jerome Bellegarda, Joe Futoma, Luca Zappella, Miguel Angel Bautista Martin, Sachin Agarwal, Sachin Mehta, and Tatiana Likhomanenko are reviewers for this conference.
Marian Stewart Bartlett is the treasurer for this conference.
Samy Bengio is a senior area chair and board member for this conference.
Kunal Talwar and Laurent Dinh are area chairs for this conference.
Arno Blaas co-organized the I Can't Believe It's Not Better: Understanding Deep Learning Through Empirical Falsification workshop.
Luca Zappella is a member of the coordination team for the I Can't Believe It's Not Better: Understanding Deep Learning Through Empirical Falsification workshop.
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.
Related readings and updates.
The Neural Information Processing Systems (NeurIPS) conference was held virtually on December 6 - 14. NeurIPS is a global conference focused on fostering the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects.
Apple sponsored the Empirical Methods in Natural Language Processing (EMNLP) conference, which was held in a hybrid format from November 7 to 11. EMNLP is a conference focused on natural language processing.