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NVIDIA at ICLR 2022

Check out the AI breakthroughs that NVIDIA researchers introduced at this year’s International Conference on Learning Representations (ICLR).

Explore the Work of NVIDIA Research

NVIDIA Research at ICLR

Virtual Presentations

Our accepted papers featured a range of groundbreaking research. From domain-adversarial training to denoising diffusion generative adversarial networks (GANs), explore the exceptional work we brought to the ICLR community.

Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach

Rafid Mahmood · Sanja Fidler · Marc T. Law | Paper

Domain-Adversarial Training: A Game Perspective

David Acuna · Marc T. Law · Guojun Zhang · Sanja Fidler | Paper

Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators

John Guibas · Morteza Mardani · Zongyi Li · Andrew Tao · Anima Anandkumar · Bryan Catanzaro | Paper

RelViT: Concept-Guided Vision Transformer for Visual Relational Reasoning

Xiaojian Ma · Weili Nie · Zhiding Yu · Huaizu Jiang · chaowei Xiao · Yuke Zhu · Song-Chun Zhu · Anima Anandkumar | Paper

Score-Based Generative Modeling with Critically Damped Langevin Diffusion

Tim Dockhorn · Arash Vahdat · Karsten Kreis | Paper

Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Zhisheng Xiao · Karsten Kreis · Arash Vahdat | Paper

Learning Continuous Environment Fields via Implicit Functions

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

See what happened at ICLR.

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Training and Resources

NVIDIA Developer Program

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The NVIDIA Applied Research Accelerator Program supports research projects that have the potential to make a real-world impact through deployment into GPU-accelerated applications adopted by commercial and government organizations.

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Accelerate 3D Deep Learning Research with Kaolin and Omniverse Kaolin App

The NVIDIA Omniverse^™ Kaolin app is a powerful visualization tool that simplifies and accelerates 3D deep learning research using NVIDIA’s Kaolin PyTorch library. The Kaolin app leverages the Omniverse platform, Universal Scene Description (USD) format, and NVIDIA RTX^™ rendering to visualize 3D outputs of any deep learning model as it’s training. Kaolin inspects 3D datasets to find inconsistencies, gain intuition, and render large synthetic datasets from collections of 3D data.

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Explore AI, Omniverse, and Beyond at the GTC Keynote

At GTC 2022, NVIDIA CEO Jensen Huang unveiled NVIDIA’s new computing platforms, robotics advances, and breakthroughs in virtual collaboration that are transforming every industry.

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Featured Demos

AI-Driven, Physics-Based Character Animation

Narrowing the Sim2Real Gap with NVIDIA Isaac^™ SIM

NVIDIA Maxine^™: Reinventing Real-Time Video Communications with AI

Optimizing an Ultrarapid DNA Sequencing Technique for Critical Care Patients

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