Recipients of the V100 accelerators were representatives from Carnegie Mellon University, the Chinese Academy of Sciences (CAS), IDSIA – the Swiss AI Lab, the Massachusetts Institute of Technology, MPI Tübingen, the Montreal Institute for Learning Algorithms, National Taiwan University, Oxford University, Peking University, Stanford University, Tsinghua University, the University of California Berkeley (UCB), the University of Tokyo, the University of Toronto, and the University of Washington.
"I think it's fantastic," said UCB assistant professor Sergey Levine, who works at the intersection of deep learning and robotics.
The V100 delivers more than 100 teraflops of deep learning performance.
From left, back row: Tatsuya Harada (University of Tokyo); Ben Poole (Stanford University); Aaron Courville (Montreal Institute for Learning Algorithms); Sergey Levine (UC Berkeley); Sedat Ozer (MIT). From left, front row: Marc Law (University of Toronto); Rupesh Srivastava (IDSIA); Pedro Domingos (University of Washington); Lars Mescheder (MPI Tübingen) and Jakob Foerster (Oxford University).
"Our NVAIL partners are at the forefront of AI, making new discoveries every day that can benefit our lives."
Nvidia also used the meet-up to announce the Nvidia Pioneering Research Awards to mark the acceptance of NVAIL partners' research papers at conferences such as ICML.
The inaugural winners included:
- Carnegie Mellon University: Improved Variational Autoencoders for Text Modelling using Dilated Convolutions.
- IDSIA/Istituto Dalle Molle di Studi sull'Intelligenza Artificiale: Recurrent Highway Networks.
- Massachusetts Institute of Technology: Coresets for Vector Summarization with Applications to Network Graphs.
- Montreal Institute for Learning Algorithms: A Closer Look at Memorization in Deep Networks.
- Tsinghua University: Identify the Nash Equilibrium in Static Games with Random Payoffs.
- University of California, Berkeley: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.
- University of Tokyo: Asymmetric Tri-training for unsupervised domain adaptation.
- University of Toronto: Deep Spectral Clustering Learning.
"I am very excited and honoured to receive this award," said the University of Tokyo's Professor Tatsuya Harada, while Levine said, "It's really great to see that Nvidia is really so involved in research, that they invite us out here and that they look at the kind of papers we are writing and recognise that."