NVIDIA at NeurIPS 2020

At the forefront of AI innovation, NVIDIA continues to push the boundaries of technology in machine learning, self-driving cars, robotics, graphics, and more. NVIDIA researchers will present 28 papers at NeurIPS from December 6 to December 12, 2020. Join us to see the latest advancements in research.

Introducing the NVIDIA Applied Research Acceleration Program

NVIDIA Applied Research Acceleration Program

Accelerating Applied Research and Innovation Together

The NVIDIA Applied Research Accelerator Program promotes innovation by supporting research with technical guidance, hardware, and funding grants for projects with the potential to make a real-world impact through deployment into GPU-accelerated applications.



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. 

Explore all of our job openings, including internships and new college graduate positions, or join us for a live session to meet with our managers.

Calling All Students

Calling All Students

Join the NVIDIA Developer Program and Enter Our NeurIPS Contest for a Chance to Win a GPU.

Excited about NeurIPS? Tweet the paper, workshop, or demo you’re most interested in checking out, tag @NVIDIAAI, and include #NeurIPS2020—you could win an NVIDIA® TITAN RTX GPU. The contest runs December 4–12 and is open to all students who have registered for the NVIDIA Developer Program with a verified university (.edu) email address. See full contest terms and conditions.

Not a member yet? JOIN NOW ›


AI Training Emulates Artwork with Limited Datasets

AI Training Emulates Artwork with Limited Datasets

The latest AI model uses generative adversarial networks to study images and emulate rare artwork using a small dataset from the Metropolitan Museum of Art. 


NVIDIA Maxine Brings AI to Video Conferencing

NVIDIA Maxine Brings AI to Video Conferencing

New AI breakthroughs in video conferencing slash bandwidth use while making it possible to reanimate faces, correct gaze, and animate characters for immersive and engaging meetings.


Real-Time Conversational AI at Scale

Real-Time Conversational AI at Scale

NVIDIA Jarvis offers state-of-the-art deep learning models and delivers intelligent, human-like responses in real time. Jarvis running at scale on GPUs is 2X faster and one-third the cost compared to models running on CPUs.


The Factory of the Future

The Factory of the Future

NVIDIA Isaac™ accelerates the process of robotics development, simulation, and deployment. Watch robots demonstrate perception and keen motor skills in the factory.  


NVIDIA DRIVE Sim Software Built on NVIDIA Omniverse

NVIDIA DRIVE Sim Software Built on NVIDIA Omniverse

NVIDIA DRIVE Sim™ software, now built on NVIDIA Omniverse™, shows how software-defined cars with AI cockpits and autonomous driving capabilities will transform transportation.


NVIDIA Clara Guardian Virtual Patient Assistant

NVIDIA Clara Guardian Virtual Patient Assistant

The NVIDIA Clara™ Guardian virtual patient assistant uses conversational AI pre-trained models and NVIDIA Fleet Command™ to increase operational efficiency and improve patient satisfaction in smart hospitals. 


Accepted Papers


NVAE: A Deep Hierarchical Variational Autoencoder

Arash Vahdat, Jan Kautz  |  Paper

A Causal View of Compositional Zero-Shot Recognition

Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik​​  |  Paper

Curriculum by Smoothing​​​​​

Samarth Sinha, Animesh Garg, Hugo Larochelle​​​​​​  |  Paper

BONGARD-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning​

Weili Nie, Zhiding Yu, Lei Mao, Ankit Patel, Yuke Zhu, Anima Anandkumar​​​​​​​  |  Paper

ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks​

Shuxuan Guo, Jose M. Alvarez,  Mathieu Salzmann​​​​​​​  |  Paper

Training Generative Adversarial Networks with Limited Data​

Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, Timo Aila​  |  Paper


Online Adaptation for Consistent Mesh Reconstruction in the Wild

Xueting Li, Sifei Liu, Shalini De Mello, Kihwan Kim, Xiaolong Wang, Ming-Hsuan Yang, Jan Kautz  |  Paper

Self-Learning Transformations for Improving Gaze and Head Redirection​

Yufeng Zheng, Seonwook Park, Xucong Zhang, Shalini De Mello, Otmar Hilliges  |  Paper

Convolutional Tensor-Train LSTM for Spatio-Temporal Learning

Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz, Anima Anandkumar  |  Paper

Generative View Synthesis: From Single-View Semantics to Novel-View Images

Tewodros Habtegebrial, Varun Jampani, Orazio Gallo, Didier Stricker  |  Paper

On the Distance Between Two Neural Networks and the Stability of Learning

Jeremy Bernstein, Arash Vahdat, Yisong Yue, Ming-Yu Liu​  |  Paper

Accelerating Reinforcement Learning Through GPU Atari Emulation

Steven Dalton, Iuri Frosio, Michael Garland​  |  Paper

Neural FFTs for Universal Texture Image Synthesis​

Morteza Mardani, Guilin Liu, Aysegul Dundar, Edward (Shiqiu) Liu, Andrew Tao, Bryan Catanzaro​  |  Paper link coming soon

Set2Graph: Learning Graphs from Sets​​

Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman​​​​  |  Paper

Online Planning with Lookahead Policies /​ Multi-Step Greedy and Approximate Real-Time Dynamic Programming​​​

Yonathan Efroni, Mohammad Ghavamzadeh, Shie Mannor​​​​​  |  Paper

Causal Discovery in Physical Systems from Videos​​​

Yunzhu Li, Antonio Torralba, Animashree Anandkumar, Dieter Fox, Animesh Garg​​​​​  |  Paper

Counterfactual Data Augmentation Using Locally Factored Dynamics​​​​

Silviu Pitis, Elliot Creager, Animesh Garg​​​​​​  |  Paper

Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning

Anthony Tompkins, Rafael Oliveira, Fabio Ramos​​​​​​​  |  Paper

Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?​

Vitaly Kurin, Saad Godil, Shimon Whitestone, Bryan Catanzaro​​​​​​​  |  Paper

Neural Networks with Recurrent Generative Feedback

Yujia Huang , James Gornet, Sihui Dai, Zhiding Yu, Tan Nguyen, Doris Tsao, Anima Anandkumar​​​​​​​  |  Paper

Learning Compositional Functions via Multiplicative Weight Updates

Jeremy Bernstein, Jiawei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar, Yisong Yue​​​​​​​  |  Paper

Learning Deformable Tetrahedral Meshes for 3D Reconstruction​

Jun Gao, Wenzheng Chen, Tommy Xiang, Alec Jacobson, Morgan McGuire, Sanja Fidler​​​​​​​  |  Paper

Variational Amodal Object Completion for Interactive Scene Editing​

Huan Ling, David Acuna, Karsten Kreis, Seung Kim, Sanja Fidler​​​​​​​  |  Paper link coming soon

Ultrahyperbolic Representation Learning

Marc Law, Jos Stam​​​​​​​  |  Paper

Self-Supervised​ Generative Adversarial Compression​

Chong Yu, Jeff Pool​​​​​​​  |  Paper

Multipole Graph Neural Operator for Parametric Partial Differential Equations

Zongyi Li ,Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew Stuart, Kaushik Bhattacharya, Anima Anandkumar​  |  Paper

GANSpace: Discovering Interpretable GAN Controls​

Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, Sylvain Paris​  |  Paper

Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems​​

Ali Sahin Lale,Kamyar Azizzadenesheli, Babak Hassibi,· Anima Anandkumar​​  |  Paper

GPU Accelerated Exhaustive Search for Optimal Ensemble of Black-Box Optimization Algorithms

Jiwei Liu, Bojan Tunguz, Gilberto Titericz | Paper


Black in AI

Black in AI is a place for sharing ideas, fostering collaborations, and discussing initiatives to increase the presence of black people in the field of artificial intelligence. Our initiatives include an academic positions program, events at various conferences related to AI, advocacy, and community building.  |  Workshop December 7, 2020

LatinX in AI

​The LatinX in AI (LXAI) bridges communities, academics, industry, and politicians working to further AI innovation and resources for LatinX individuals globally. We host research workshops at AI academic conferences and drive and support research, development, and infrastructure programs to boost innovation and capabilities of Latin Americans working in artificial intelligence.  |  Workshop December 7, 2020

Queer in AI

Queer in AI’s mission to make the AI and machine learning community one that welcomes, supports, and values queer scientists. We accomplish this by building a visible community of queer and ally AI and machine learning scientists through meetups, poster sessions, mentoring, and other initiatives.  |  Workshop December 8, 2020

Women in Machine Learning

​Women in Machine Learning (WiML) works to increase awareness and appreciation of the achievements of women in machine learning. Our programs help women build their technical confidence and their voice, and our publicity efforts help ensure that women in machine learning and their achievements are known in the community.  |   Workshop December 9, 2020


The goal of the Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop is to disseminate relevant, parallel findings in the fields of computational neuroscience, psychology, and cognitive science that may inform modern machine learning methods.  |  Workshop ​December 12, 2020

See the Leading AI and Data Science Startups

Over 6,500 of the world’s leading AI and data science startups are accelerated by NVIDIA Inception, a program that provides go-to-market support, expertise, and technology for the next generation of visionaries. See the NVIDIA Inception members sponsoring NeurIPS 2020.



Deep learning platform that gears up your trained neural networks to become production-grade solutions on any hardware, at scale.


Element AI

Element AI

AI-based automation platform that  improves efficiencies in retail, insurance, logistics, manufacturing and more.


Licensed AI software throughout the L2-L4 autonomous driving stack: perception, intent modeling, path planning and vehicle control.




High quality training and validation data for AI applications


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