no code implementations • 25 May 2022 • Yili Shen, Xiao Liu, Cheng-Wei Ju, Jiaxu Yan, Jun Yi, Zhou Lin, Hui Guan
Subgraph representation learning based on Graph Neural Network (GNN) has exhibited broad applications in scientific advancements, such as predictions of molecular structure-property relationships and collective cellular function.
no code implementations • 16 Feb 2022 • Dharma Shukla, Muthian Sivathanu, Srinidhi Viswanatha, Bhargav Gulavani, Rimma Nehme, Amey Agrawal, Chen Chen, Nipun Kwatra, Ramachandran Ramjee, Pankaj Sharma, Atul Katiyar, Vipul Modi, Vaibhav Sharma, Abhishek Singh, Shreshth Singhal, Kaustubh Welankar, Lu Xun, Ravi Anupindi, Karthik Elangovan, Hasibur Rahman, Zhou Lin, Rahul Seetharaman, Cheng Xu, Eddie Ailijiang, Suresh Krishnappa, Mark Russinovich
At the heart of Singularity is a novel, workload-aware scheduler that can transparently preempt and elastically scale deep learning workloads to drive high utilization without impacting their correctness or performance, across a global fleet of AI accelerators (e. g., GPUs, FPGAs).
no code implementations • ICLR 2019 • Wenpeng Hu, Zhou Lin, Bing Liu, Chongyang Tao, Zhengwei Tao, Jinwen Ma, Dongyan Zhao, Rui Yan
Several continual learning methods have been proposed to address the problem.
no code implementations • ICLR 2019 • Wenpeng Hu, Zhengwei Tao, Zhanxing Zhu, Bing Liu, Zhou Lin, Jinwen Ma, Dongyan Zhao, Rui Yan
A large amount of parallel data is needed to train a strong neural machine translation (NMT) system.