Xiaodong Yang

[Jan Kautz @jankautz]

My team at @NVIDIAAI has three #ECCV papers today.

"Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification" with our collaborators from CMU.
PDF: https://t.co/VrVwo2JCPH
ECCV link: https://t.co/L29pzdamDd (1/3)

[NVIDIA AI Developer @NVIDIAAIDev]

To help make transportation systems smarter, researchers at #CVPR2020 competed in the fourth annual #AI City Challenge using NVIDIA GPUs for both training and inference. Learn more about the challenge and this year’s winners: https://t.co/dl6Dy11F0C

[hardmaru @hardmaru]

“Time is an illusion.” ― Albert Einstein

[NVIDIA AI Developer @NVIDIAAIDev]

Meet the AI choreographer! To help automatically create a dance video, @NVIDIA researchers developed a #deeplearning model that can automatically compose new dance moves. The work is being presented at #NeurIPS2019 this week.

[hardmaru @hardmaru]

Dancing to Music

This paper looks at generating dance from music through a decomposition to composition learning framework. #NeurIPS2019

Paper https://t.co/q4ojHXOw76
Project https://t.co/4Vxi8um9J6

[Jan Kautz @jankautz]

@NVIDIA’s DGX SuperPOD breaks #AI performance records. Ready-to-run infrastructure with leading performance in #MLPerf: https://t.co/D6P5eVzB8A

[DeepLearning.AI @DeepLearningAI]

"Nvidia has more software engineers than hardware engineers. A GPU is a lot more than just a chip. It gets good performance through amazing compilers, libraries, frameworks, and applications.” Learn more about Bryan Catanzaro @ctnzr’s work at @Nvidia: https://t.co/6T4LesFN9D

[NVIDIA AI @NVIDIAAI]

At #CVPR2019, NVIDIA Researchers will present 20 accepted papers, including 11 orals. Check out the list here: https://t.co/0X0POX24RR

[Jan Kautz @jankautz]

#NVIDIA Turing GPUs support optical flow - great for activity recognition! https://t.co/aZOxT9UE7B

[NVIDIA Robotics @NVIDIARobotics]

There’s still time to participate in the AI City Challenge with plenty of great prizes for winners of all three challenge tracks. Learn more: https://t.co/5zDg0c4YNE

[NVIDIA Robotics @NVIDIARobotics]

We just launched the 3rd AI City Challenge and looking for teams to participate. Learn more about how you can push the boundaries for AI-based traffic optimization. https://t.co/YxSMH55I0y

[NVIDIA AI Developer @NVIDIAAIDev]

To enable the next generation of robots, NVIDIA is opening a new #AI robotics research lab in Seattle led by Dieter Fox. See how they are designing a robotic kitchen assistant: https://t.co/NrZ0A39psK

[hardmaru @hardmaru]

A Style-Based Generator Architecture for Generative Adversarial Networks. @NvidiaAI does it again! https://t.co/W2Av34ta9O

[NVIDIA AI Developer @NVIDIAAIDev]

NVIDIA set records for six #MLPerf single-node and at-scale #AI benchmarks using Tensor Core GPUs – read details about the results: https://t.co/vuGPeFhqpR

[Julie Bernauer @JulieB_NV]

NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch | NVIDIA Developer Blog https://t.co/7NFNfmUvzY

[Jan Kautz @jankautz]

NVIDIA is launching an AI research residency program: https://t.co/gxRnHnAMiI. Consider applying! @NvidiaAI #ai #research

[Google AI @GoogleAI]

Check out Fluid Annotation, an exploratory machine learning–powered interface for annotating the class label and outline of every object and background region in an image, accelerating the creation of labeled datasets by a factor of 3x. https://t.co/aUPNBOvUH8

[Anaconda @anacondainc]

Today @NVIDIA launched the RAPIDS GPU Acceleration Platform for large-scale data analytics & machine learning. Anaconda helped NVIDIA develop these new capabilities, which will be available through our public package repository & in Anaconda Enterprise. https://t.co/cuVMwjiqVE

[Google AI @GoogleAI]

Announcing the launch of Dataset Search, a new way for researchers to find the datasets they need, wherever they’re hosted, whether it’s a publisher's site, a digital library, or an author's personal web page. Learn more at https://t.co/tpdOkjAVgW

[Clément Farabet @clmt]

Proud of the team at NVIDIA for hitting 2 first places on vision challenges at CVPR — domain adaptation is a big topic for autonomous vehicle perception. Cross-team effort across research and applied AV group.
https://t.co/mN6yNd9Iwo cc @aysegl_dndr