NVIDIA at NeurIPS 2023

Dec 10–16

Intro
Presentations
Trainings and Resources
Resources
NVIDIA Inception Members

Intro
Presentations
Trainings and Resources
Resources
NVIDIA Inception Members

At the forefront of AI innovation, NVIDIA continues to push the boundaries of technology in machine learning, self-driving cars, robotics, graphics, simulation, and more. NVIDIA researchers will present groundbreaking papers at NeurIPS from December 10–16. Join us to see the latest advancements in research.

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When you work at NVIDIA, you have the opportunity to solve some of the world’s hardest problems and discover never-before-seen ways to improve the quality of life for people everywhere—from healthcare to robots, self-driving cars to blockbuster movies, and a growing list of new opportunities every single day.

Learn more about our career opportunities by exploring current job openings and university jobs.

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Presentations

Our accepted papers feature a range of groundbreaking research. From alias-free general adversarial networks (GANs) that create photorealistic images to semantic segmentation with transformers, explore the exceptional work we’re bringing to the NeurIPS community.

Papers
Posters
Workshops

Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects

Chuanruo Ning · Ruihai Wu · Haoran Lu · Kaichun Mo · Hao Dong | Paper

Syntactic Binding in Diffusion Models: Enhancing Attribute Correspondence Through Attention Map Alignment

Royi Rassin · Eran Hirsch · Daniel Glickman · Shauli Ravfogel · Yoav Goldberg · Gal Chechik | Paper

Norm-Guided Latent Space Exploration for Text-to-Image Generation

Dvir Samuel · Rami Ben-Ari · Nir Darshan · Haggai Maron · Gal Chechik | Paper

SceneScape: Text-Driven Consistent Scene Generation

Rafail Fridman · Amit Abecasis · Yoni Kasten · Tali Dekel | Paper

Point Cloud Completion With Pretrained Text-to-Image Diffusion Models

Yoni Kasten · Ohad Rahamim · Gal Chechik | Paper

Train Hard, Fight Easy: Robust Meta Reinforcement Learning

Ido Greenberg · Shie Mannor · Gal Chechik · Eli Meirom | Paper

Expressive Sign Equivariant Networks for Spectral Geometric Learning

Derek Lim · Joshua Robinson · Stefanie Jegelka · Haggai Maron | Paper

Optimization or Architecture: What Matters in Non-Linear Filtering?

Ido Greenberg · Netanel Yannay · Shie Mannor | Paper

Policy Gradient for Rectangular Robust Markov Decision Processes

Navdeep Kumar · Esther Derman · Matthieu Geist · Kfir Y. Levy · Shie Mannor | Paper

Individualized Dosing Dynamics via Neural Eigen Decomposition

Stav Belogolovsky · Ido Greenberg · Danny Eytan · Shie Mannor | Paper

trajdata: A Unified Interface to Multiple Human Trajectory Datasets

Boris Ivanovic · Guanyu Song · Igor Gilitschenski · Marco Pavone | Paper

PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction

Apoorva Sharma · Sushant Veer · Asher Hancock · Heng Yang · Marco Pavone · Anirudha Majumdar | Coming Soon

Generalizable One-shot Neural Head Avatar

Xueting Li · Shalini De Mello · Sifei Liu · Koki Nagano · Umar Iqbal · Jan Kautz | Paper

Convolutional State Space Models for Long-Range Spatiotemporal Modeling

Jimmy Smith · Shalini De Mello · Jan Kautz · Scott Linderman · Wonmin Byeon | Coming Soon

Geometry-Informed Neural Operator for Large-Scale 3D PDEs

Zongyi Li · Nikola Kovachki · Chris Choy · Boyi Li · Jean Kossaifi · Shourya Otta · Mohammad Amin Nabian · Maximilian Stadler · Christian Hundt · Kamyar Azizzadenesheli · Animashree Anandkumar | Paper

P-Flow: A Fast and Data-Efficient Zero-Shot TTS Through Speech Prompting

Sungwon Kim · Kevin Shih · rohan badlani · Joao Felipe Santos · Evelina Bakhturina · Mikyas Desta · Rafael Valle · Sungroh Yoon · Bryan Catanzaro | Coming Soon

ClimSim: An Open Large-Scale Dataset for Training High-Resolution Physics Emulators in Hybrid Multi-Scale Climate Simulators

Sungduk Yu · Walter Hannah · Liran Peng · Jerry Lin · Mohamed Aziz Bhouri · Ritwik Gupta · Björn Lütjens · Justus C. Will · Gunnar Behrens · Nora Loose · Charles Stern · Tom Beucler · Bryce Harrop · Benjamin Hillman · Andrea Jenney · Savannah L. Ferretti · Nana Liu · Animashree Anandkumar · Noah Brenowitz · Veronika Eyring · Nicholas Geneva · Pierre Gentine · Stephan Mandt · Jaideep Pathak · Akshay Subramaniam · Carl Vondrick · Rose Yu · Laure Zanna · Ryan Abernathey · Fiaz Ahmed · David Bader · Pierre Baldi · Elizabeth Barnes · Christopher Bretherton · Julius Busecke · Peter Caldwell · Wayne Chuang · Yilun Han · YU HUANG · Fernando Iglesias-Suarez · Sanket Jantre · Karthik Kashinath · Marat Khairoutdinov · Thorsten Kurth · Nicholas Lutsko · Po-Lun Ma · Griffin Mooers · J. David Neelin · David Randall · Sara Shamekh · Mark Taylor · Nathan Urban · Janni Yuval · Guang Zhang · Tian Zheng · Mike Pritchard | Paper

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Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects

Chuanruo Ning · Ruihai Wu · Haoran Lu · Kaichun Mo · Hao Dong | Poster

Syntactic Binding in Diffusion Models: Enhancing Attribute Correspondence Through Attention Map Alignment

Royi Rassin · Eran Hirsch · Daniel Glickman · Shauli Ravfogel · Yoav Goldberg · Gal Chechik | Poster

Norm-Guided Latent Space Exploration for Text-to-Image Generation

Dvir Samuel · Rami Ben-Ari · Nir Darshan · Haggai Maron · Gal Chechik | Poster

SceneScape: Text-Driven Consistent Scene Generation

Rafail Fridman · Amit Abecasis · Yoni Kasten · Tali Dekel | Poster

Point Cloud Completion With Pretrained Text-to-Image Diffusion Models

Yoni Kasten · Ohad Rahamim · Gal Chechik | Poster

Train Hard, Fight Easy: Robust Meta Reinforcement Learning

Ido Greenberg · Shie Mannor · Gal Chechik · Eli Meirom | Poster

Expressive Sign Equivariant Networks for Spectral Geometric Learning

Derek Lim · Joshua Robinson · Stefanie Jegelka · Haggai Maron | Poster

Optimization or Architecture: What Matters in Non-Linear Filtering?

Ido Greenberg · Netanel Yannay · Shie Mannor | Poster

Policy Gradient for Rectangular Robust Markov Decision Processes

Navdeep Kumar · Esther Derman · Matthieu Geist · Kfir Y. Levy · Shie Mannor | Poster

Individualized Dosing Dynamics via Neural Eigen Decomposition

Stav Belogolovsky · Ido Greenberg · Danny Eytan · Shie Mannor | Poster

trajdata: A Unified Interface to Multiple Human Trajectory Datasets

Boris Ivanovic · Guanyu Song · Igor Gilitschenski · Marco Pavone | Poster

PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction

Apoorva Sharma · Sushant Veer · Asher Hancock · Heng Yang · Marco Pavone · Anirudha Majumdar| Poster

Generalizable One-shot Neural Head Avatar

Xueting Li · Shalini De Mello · Sifei Liu · Koki Nagano · Umar Iqbal · Jan Kautz| Poster

Convolutional State Space Models for Long-Range Spatiotemporal Modeling

Jimmy Smith · Shalini De Mello · Jan Kautz · Scott Linderman · Wonmin Byeon| Poster

Geometry-Informed Neural Operator for Large-Scale 3D PDEs

P-Flow: A Fast and Data-Efficient Zero-Shot TTS Through Speech Prompting

Sungwon Kim · Kevin Shih · rohan badlani · Joao Felipe Santos · Evelina Bakhturina · Mikyas Desta · Rafael Valle · Sungroh Yoon · Bryan Catanzaro| Poster

ClimSim: An Open large-Scale Dataset for Training High-Resolution Physics Emulators in Hybrid Multi-Scale Climate Models

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Workshop on Diffusion Models

Yang Song · Tali Dekel · Sayak Paul · Brian Trippe · Jason Yim · Holden Lee · Hyungjin Chung · Shuang Li · Gowthami Somepalli · Yang Song · Arash Vahdat ·Ruiqi Gao ·Tim Salimans · Robin Rombach | Room 242 | Workshop

New Frontiers of AI for Drug Discovery and Development

Animashree Anandkumar · Ilija Bogunovic · Ti-chiun Chang · Quanquan Gu · Jure Leskovec · Michelle Li · Chong Liu · Nataša Tagasovska · Wei Wang | Room 242 | Workshop

Foundation Models for Decision Making

Mengjiao (Sherry) Yang · Ofir Nachum · Yilun Du · Stephen McAleer · Igor Mordatch · Linxi Fan · Jeannette Bohg · Dale Schuurmans | Hall E2 | Workshop

Workshop on Advancing Neural Network Training (WANT): Computational Efficiency, Scalability, and Resource Optimization

Julia Gusak · Jean Kossaifi · Alena Shilova · Cristiana Bentes · Animashree Anandkumar · Olivier Beaumont | Room 243 - 245 | Workshop

The Symbiosis of Deep Learning and Differential Equations -- III

Luca Celotti · Martin Magill · Ermal Rrapaj · Winnie Xu · Qiyao Wei · Archis Joglekar · Michael Poli · Animashree Anandkumar | Room 255 - 257 | Workshop

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Trainings and Resources

Get Hands-On Training With the NVIDIA Deep Learning Institute

Develop and master the skills you need in AI, accelerated computing, and the metaverse through the NVIDIA Deep Learning Institute (DLI).

Claim Your Free Course

Instructor-Led Workshops

Take a hands-on workshop led by the NVIDIA certified instructors, and earn a certificate.

Receive 25% off the DLI virtual workshops offered through February 2024 with code DLINEURIPS23. Space is limited.

View Workshop Schedule

Self-Paced Courses

Learn anytime, anywhere, with just a computer and an internet connection.

View All Self-Paced Courses

University Ambassador Program

As an ambassador you can bring free instructor-led workshops in cutting-edge technologies-Al, accelerated computing, data science, and more-to your university, enriching your curriculum and giving your students the skills they need to jumpstart their future.

Apply Now

Higher Education and Research Resources

Explore career-enhancing NVIDIA resources, including Teaching Kits, webinars, events, and more, by visiting our developer resource hub.

Learn More

Access AI Models and Connect with Experts

Join our free program to gain access to SOTA AI models, 600+ SDKs, training, expert community forums, and technical resources that can help accelerate your life’s work. Build on your technical knowledge or learn more about a new technology by taking advantage of a free self-paced course when you join.

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Additional Resources

Accelerate your Startup

NVIDIA Inception provides startups with access to the latest developer resources, preferred pricing on NVIDIA software and hardware, and exposure to the venture capital community. The program is free and available for tech startups of all stages.

Learn More and Apply

Try State-of-the-Art Generative AI Models From Your Browser

NVIDIA AI Playground offers an easy-to-use interface to quickly experience generative AI models from your browser without any setup.

Try the AI Playground

Turn 2D Images into 3D Objects for Virtual Worlds

This technology allows for the creation of detailed 3D replicas of real-world objects using just a smartphone camera. The resulting models can be edited and used in variou s applications such as digital twins, robotics, and game development.

Learn More About Neuralangelo

Meet NVIDIA Inception Members

NVIDIA Inception is a free program that helps startups evolve faster through access to cutting-edge technology and NVIDIA experts, connections with venture capitalists, and co-marketing support. Explore the members who will be at NeurIPS this year.