content type eventpublished December 1, 2023
research area General

Neural Information Processing Systems (NeurIPS) 2023

Apple sponsored the Neural Information Processing Systems (NeurIPS) conference, which took place in person from December 10 to 16 in New Orleans, Louisiana. NeurIPS is a conference that fosters 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 2023.

Schedule

Sunday, December 10

Monday, December 11

  • WORKSHOP
  • Black in AI
  • 8:15 AM - 3:30 PM CST, Room R02-R05
  • WORKSHOP
  • LatinX in AI
  • 8:15 AM - 3:30 PM CST, Room 217-219
  • Olga Jumbo Sanchez will be participating in the mentoring hour.
  • WORKSHOP
  • Queer in AI
  • 9:30 AM - 3:30 PM CST, Room R06-R09
  • WORKSHOP
  • Women in Machine Learning
  • 8:00 AM - 3:30 PM CST, Great Hall
  • Derek Smith will be hosting a roundtable on Internships at Apple. Samy Bengio and Tatiana Likhomenko will be hosting a roundtable on Research at Apple

Tuesday, December 12

Wednesday, December 13

  • POSTER
  • How to Scale Your EMA
  • 10:45 AM CST in Great Hall & Hall B1+B2 #1000
  • Dan Busbridge, Jason Ramapuram, Pierre Ablin, Tatiana Likhomanenko, Eeshan Gunesh Dhekane, Xavier Suau, Russ Webb
  • POSTER
  • 4M: Massively Multimodal Masked Modeling
  • 5:00 - 7:00 PM CST in Great Hall & Hall B1+B2 #1022
  • David Mizrahi, Roman Bachmann, Oguzhan Fatih Kar, Teresa Yeo, Mingfei Gao, Afshin Dehghan, Amir Zamir
  • POSTER
  • Adaptive Weight Decay
  • 5:00 - 7:00 PM CST in Great Hall & Hall B1+B2 #721
  • Amin Ghiasi, Ali Shafahi, Reza Ardekani

Thursday, December 14

  • ORAL
  • DataComp: In Search of the Next Generation of Multimodal Datasets
  • 10:30 - 10:45 AM CST, Room R06-R09
  • Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt
  • POSTER
  • DataComp: In Search of the Next Generation of Multimodal Datasets
  • 10:45 AM - 12:45 PM CST, Great Hall & Hall B1+B2 (level 1) #439
  • Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt

Friday, December 15

  • WORKSHOP
  • Diffusion Models
  • 8:50 AM - 5:30 PM, Hall B1
  • James Thorton and Bahjat Kawar helped organize this workshop
  • Manifold Diffusion Fields
  • Ahmed Elhag, Yuyang Wang, Josh Susskind, Miguel Angel Bautista Martin

Saturday, December 16

Accepted Papers

4M: Massively Multimodal Masked Modeling

David Mizrahi, Roman Bachmann, Oguzhan Fatih Kar, Teresa Yeo, Mingfei Gao, Afshin Dehghan, Amir Zamir

Adaptive Weight Decay

Amin Ghiasi, Ali Shafahi, Reza Ardekani

Agnostically Learning Single-Index Models using Omnipredictors

Aravind Gollakota, Parikshit Gopalan, Adam R. Klivans, Konstantinos Stavropoulos

DataComp: In search of the next generation of multimodal datasets

Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt

DeepPCR: Parallelizing Sequential Operations in Neural Networks

Federico Danieli, Miguel Sarabia, Xavier Suau, Pau Rodríguez, Luca Zappella

Fast Optimal Locally Private Mean Estimation via Random Projections

Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar

How to Scale Your EMA

Dan Busbridge, Jason Ramapuram, Pierre Ablin, Tatiana Likhomanenko, Eeshan Gunesh Dhekane, Xavier Suau, Russ Webb

PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model

Yizhe Zhang, Jiatao Gu, Zhuofeng Wu, Shuangfei Zhai, Josh Susskind, Navdeep Jaitly

PDP: Parameter-free Differentiable Pruning is All You Need

Minsik Cho, Saurabh Adya, Devang Naik

Characterizing Omniprediction via Multicalibration

Parikshit Gopalan, Michael P. Kim, Omer Reingold

Pre-trained Language Models Do Not Help Auto-regressive Text-to-Image Generation

Yuhui Zhang, Brandon McKinzie, Vaishaal Shankar, Zhe Gan, Alexander Toshev

When Does Optimizing a Proper Loss Yield Calibration?

Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran

Workshop Accepted Papers

Bootstrap Your Own Variance

Polina Turishcheva, Jason Ramapuram, Sinead Williamson, Dan Busbridge, Eeshan Dhekane, Russ Webb

Controllable Music Production with Diffusion Models and Guidance Gradients

Mark Levy, Bruno Di Giorgi, Floris Weers, Angelos Katharopoulos, Tom Nickson

Diffusion Models as Masked Audio-Video Learners

Elvis Nunez, Yanzi Jin, Mohammad Rastegari, Sachin Mehta, Maxwell Horton

Federated Learning for Speech Recognition: Revisiting Current Trends Towards Large-Scale ASR

Sheikh Shams Azam, Martin Pelikan, Vitaly Feldman, Kunal Talwar, Honza Silovsky, Tatiana Likhomanenko

Generating Molecular Conformers with Manifold Diffusion Fields

Yuyang Wang, Ahmed Elhag, Navdeep Jaitly, Josh Susskind, Miguel Angel Bautista Martin

Manifold Diffusion Fields

Ahmed Elhag, Yuyang Wang, Josh Susskind, Miguel Angel Bautista Martin

Multimodal Data and Resource Efficient Device-Directed Speech Detection with Large Foundation Models

Dominik Wagner, Alex Churchill, Siddharth Sigtia, Panos Georgiou, Matt Mirsamadi, Aarshee Mishra, Erik Marchi

ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Iman Mirzadeh, Keivan Alizadeh, Sachin Mehta, Carlo C Del Mundo, Oncel Tuzel, Golnoosh Samei, Mohammad Rastegari, Mehrdad Farajtabar

SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding

Haoxiang Wang, Pavan Kumar Anasosalu Vasu, Fartash Faghri, Raviteja Vemulapalli, Mehrdad Farajtabar, Sachin Mehta, Mohammad Rastegari, Oncel Tuzel, Hadi Pouransari

TiC-CLIP: Continual Training of CLIP Models

Saurabh Garg, Hadi Pour Ansari, Mehrdad Farajtabar, Sachin Mehta, Raviteja Vemulapalli, Oncel Tuzel, Vaishaal Shankar, Fartash Faghri

LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures

Vimal Thilak, Omid Saremi, Preetum Nakkiran, Josh Susskind, Chen Huang, Hanlin Goh, Laurent Dinh, Etai Littwin

What Algorithms can Transformers Learn? A Study in Length Generalization

Hattie Zhou, Omid Saremi, Etai Littwin, Arwen Bradley, Noam Razin, Josh Susskind, Samy Bengio, Preetum Nakkiran

Demos

On-device Personal Voice for Accessibility

On-device ML model training is a key research area we focus on. In this demo, we will share how we applied text-to-speech model adaptation technology to Apple devices to build the personal voice with a limited number of recordings so that the people who are at risk of losing their voice can store their voice and use it in live speech when they are not able to speak.

Learn more about the machine learning research behind Personal Voice here.

Back to Back Siri Requests

Siri Directed Speech Detector (SDSD) runs completely on device. It makes use of information from acoustics, automatic speech recognition system, and lexical information to decide if an utterance is directed towards Siri or not. With this feature, Siri listens to user speech for a subsequent request while it is responding to the previous request.

All NeurIPS attendees were invited to stop by the Apple booth (booth number 1103, located in Hall C and D of the New Orleans Ernest N. Morial Convention Center) to experience these demos in person.

Acknowledgements

Samy Bengio is a member of the NeurIPS 2023 Board.

Marian Stewart Bartlett is the NeurIPS 2023 Treasurer.

Alexander Toshev, Kunal Talwar, Navdeep Jaitly, Pierre Ablin, Preetum Nakkiran, Shuangfei Zhai, Vaishaal Shankar, Vitaly Feldman, and Yizhe Zhang are Area Chairs for NeurIPS 2023.

Marco Cuturi and Samy Bengio are NeurIPS 2023 Senior Area Chairs.

Audra McMillan is an Ethics Reviewer for NeurIPS 2023.

Tatiana Likhomanenko and Oncel Tuzel are NeurIPS 2023 Datasets and Benchmarks Area Chairs.

Aravind Gollakota, Arno Blaas, Barry Theobald, Bhavika Devnani, Bogdan Mazoure, Enrico Fini, Etai Littwin, Hilal Asi, Jason Ramapuram, Lyndon Duong, Miguel Sarabia, Pau Rodriguez, Rahul Sunil Bhalerao, Richard Bai, Shams Azam, Skyler Seto, Tatiana Likhomanenko, Vimal Thilak, and Xavi Suau are reviewers for NeurIPS 2023.

Related readings and updates.

International Conference on Machine Learning (ICML) 2023

Apple sponsored the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), which took place in person from June 18 to 22 in Vancouver, Canada. CVPR is an annual computer vision event comprising the main conference and several co-located workshops and courses. Below was the schedule of our sponsored workshops and events at CVPR 2023.

See event details

NeurIPS 2022

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.

See event details